ErcinAE Governance of globalized water resources print format...Printed by Wohrmann Print Service,...

GOVERNANCE OF GLOBALIZED WATER RESOURCES

THE APPLICATION OF THE WATER FOOTPRINT TO INFORM

CORPORATE STRATEGY AND GOVERNMENT POLICY

Thesis committee members:

Prof. dr. F.Eising University of Twente, chairman and secretary

Prof. dr. ir. A.Y.Hoekstra University of Twente, promoter

Prof. dr. A.Garrido Polytechnic University of Madrid, Spain

Prof. dr. ir. N.C van de Giesen TU Delft

Prof. dr. A. van der Veen University of Twente

Prof. dr. ir. E. van Beek University of Twente

Copyright © 2012 by Ali Ertug Ercin, Enschede, the Netherlands, All rights reserved.

Cover image: Alex Aquilina, Amsterdam, the Netherlands

Printed by Wohrmann Print Service, Zutphen, the Netherlands

ISBN : 978-90-365-3415-4

DOI: 10.3990/1.9789036534154

Email: [email protected]

GOVERNANCE OF GLOBALIZED WATER RESOURCES

THE APPLICATION OF THE WATER FOOTPRINT TO INFORM CORPORATE STRATEGY AND GOVERNMENT POLICY

DISSERTATION

to obtain

the degree of doctor at the University of Twente,

on the authority of the rector magnificus,

Prof.dr. H. Brinksma,

on account of the decision of the graduation committee,

to be publicly defended

on the day Wednesday 31 October 2012 at 16.45

by

Ali Ertug Ercin

born on 23 June 1979

in Ankara, Turkey

This dissertation has been approved by:

Prof. dr. ir. A.Y Hoekstra promoter

Contents

Acknowledgments ................................................................................................................ xi

Summary ............................................................................................................................. xiii

1 Introduction ................................................................................................................. 17

1.1 The water footprint concept ............................................................................... 18

1.2 Water footprint assessment ................................................................................ 19

1.3 Problem statement .............................................................................................. 20

1.3.1 Business perspective ...................................................................................... 20

1.3.2 Governmental perspective ............................................................................. 21

1.3.3 Need for set of indicators ............................................................................... 22

1.4 Objective ............................................................................................................ 22

1.5 Research questions ............................................................................................. 23

1.6 Structure of the thesis ......................................................................................... 23

2 The water footprint of soy milk and soy burger and equivalent animal products ....... 27

2.1 Introduction ........................................................................................................ 28

2.2 Method and data ................................................................................................. 29

2.3 Results ................................................................................................................ 40

2.3.1 Water footprint of soybean ............................................................................ 40

2.3.2 Water footprint of soy products ..................................................................... 41

2.3.3 Water footprint of soy products versus equivalent animal products .............. 46

2.4 Discussions ......................................................................................................... 48

2.5 Conclusions ........................................................................................................ 50

3 Corporate water footprint accounting and impact assessment: The case of the water footprint of a sugar-containing carbonated beverage ........................................................... 53

3.1 Introduction ........................................................................................................ 53

3.2 Method ............................................................................................................... 55

3.3 Data sources and assumptions ............................................................................ 57

3.3.1 Operational water footprint ............................................................................ 58

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3.3.2 Supply-chain water footprint ......................................................................... 59

3.4 Results ................................................................................................................ 65

3.4.1 Water footprint of a 0.5 litre PET-bottle sugar-containing carbonated beverage ...................................................................................................................... 65

3.5 Impact assessment of a 0.5 litre PET-bottle sugar-containing carbonated beverage .......................................................................................................................... 71

3.6 Conclusion ......................................................................................................... 76

4 Sustainability of national consumption from a water resources perspective: A case study for France ................................................................................................................... 81

4.1 Introduction ........................................................................................................ 82

4.2 Method and data ................................................................................................. 84

4.2.1 Water footprint accounting ............................................................................ 84

4.2.2 Identifying priority basins and products ........................................................ 86

4.3 Results ................................................................................................................ 88

4.3.1 Water footprint of production ........................................................................ 88

4.3.2 Virtual water flows ........................................................................................ 94

4.3.3 Water footprint of consumption ..................................................................... 99

4.4 Priority basins and products ............................................................................. 104

4.4.1 Water footprint of production ...................................................................... 104

4.4.2 Water footprint of consumption ................................................................... 107

4.5 Discussion and conclusion ............................................................................... 114

5 Water footprint scenarios for 2050: A global analysis and case study for Europe .... 117

5.1 Introduction ...................................................................................................... 119

5.2 Method ............................................................................................................. 124

5.2.1 Scenario description ..................................................................................... 124

5.2.2 Drivers of change ......................................................................................... 126

5.2.3 Estimation of water footprints ..................................................................... 131

5.2.4 European case study ..................................................................................... 137

5.3 Global water footprint in 2050 ......................................................................... 137


5.3.2 Virtual water flows between regions ........................................................... 142


5.4 The water footprint of Europe in 2050 ............................................................. 150


5.4.2 Virtual water flows between countries ......................................................... 155


5.5 Discussion and conclusion ............................................................................... 164

Appendix 5.1: Countries and regional classification ..................................................... 167

Appendix 5.2: Population and GDP forecasts ............................................................... 168

Appendix 5.3: Coefficient for change in unit water footprint of agricultural commodities per region per scenario (α values) ................................................................................. 169

Appendix 5.4: Agricultural production changes in 2050 relative to the baseline in 2050 ....................................................................................................................................... 170

6 Understanding carbon and water footprints: similarities and contrasts in concept, method and policy response ............................................................................................... 171

6.1 Introduction ...................................................................................................... 173

6.2 Origins of the carbon and water footprint concepts ......................................... 175

6.2.1 The carbon footprint .................................................................................... 176

6.2.2 The water footprint ...................................................................................... 179

6.3 Comparison of the carbon and water footprints from a methodological viewpoint 180

6.3.1 Environmental pressure indicators ............................................................... 181

6.3.2 Units of measurement .................................................................................. 183

6.3.3 Spatial and temporal dimensions ................................................................. 183

6.3.4 Footprint components .................................................................................. 184

6.3.5 Entities for which the footprints can be calculated ...................................... 186

6.3.6 Calculation methods .................................................................................... 186

6.3.7 Scope ........................................................................................................... 191

6.3.8 Sustainability of the carbon and water footprints ........................................ 191

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6.4 Comparison of responses to the carbon and water footprints ........................... 192

6.4.1 The need for reduction: Maximum sustainable footprint levels ................... 193

6.4.2 Reduction of footprints by increasing carbon and water efficiency ............. 195

6.4.3 Reduction of footprints by changing production and consumption patterns 197

6.4.4 Offsetting, neutrality and trading ................................................................. 198

6.4.5 The interplay of actors ................................................................................. 200

6.4.6 The water–energy nexus .............................................................................. 203

6.5 Lessons to learn ................................................................................................ 204

6.6 Conclusion ....................................................................................................... 206

7 Integrating ecological, carbon and water footprint into a “footprint family” of indicators: definition and role in tracking Human Pressure on the Planet ........................ 209

7.1 Introduction ...................................................................................................... 209

7.1.1 Global environmental change: an overview ................................................. 209

7.1.2 The need for a set of indicators .................................................................... 212

7.1.3 The need for consumer approach ................................................................. 213

7.2 Methods ............................................................................................................ 214

7.2.1 Ecological footprint ..................................................................................... 214

7.2.2 Carbon footprint ........................................................................................... 216

7.2.3 Water footprint ............................................................................................. 217

7.3 Discussion ........................................................................................................ 218

7.3.1 Testing and comparing footprint indicators ................................................. 218

7.3.2 Definition of the footprint family................................................................. 223

7.3.3 The need for a streamlined ecological-economic modelling framework ..... 224

7.4 The role of the footprint family in the EU policy context ................................ 226

7.4.1 Resource use trends at EU level ................................................................... 226

7.4.2 The EU policy context ................................................................................. 227

7.4.3 Policy usefulness of the footprint family ..................................................... 228

7.5 Conclusion ....................................................................................................... 234

8 Discussions and conclusion ...................................................................................... 237

References ......................................................................................................................... 241

List of publications ............................................................................................................ 275

About the author ................................................................................................................ 277

Acknowledgments

First and foremost, I would like to express my deep and sincere gratitude to my supervisor

Prof. dr. Arjen Y. Hoekstra for providing me the opportunity to work with him and his trust

in all these years. His wide knowledge and his logical way of thinking have been of great

value for me. Arjen, I learnt many things from you professionally but the most important

one is how to think as a scientist. I always admired your dedication, intelligence and your

professionalism. I have to admit that you surprised me during our trip to Brazil – your

samba dancing and warm/friendly attitude is unforgettable! Thank you for all, especially for

believing in me.

I wish to express my warm and sincere thanks to Dr. Sahnaz Tigrek and Prof. dr. Melih

Yanmaz from Middle East Technical University for their ultimate support since my BSc

degree. They have always encouraged me to take the next steps and they have gone beyond

being my professors and become my life coaches.

During this work, I have collaborated with many colleagues for whom I have great regard,

and I wish to extend my warmest thanks to all those who have helped me with my work.

Special thanks to Mesfin, Guoping, Nicolas, Winnie, Mireia, Hatem, Blanca and Michael.

Thanks also to Joke, Brigitte and Anke for their assistance during my work. Also, special

thanks to Ruth and Alex for accepting being next to me during my defence as my

paranymphs.

My friends in the Netherlands, Turkey and other parts of the world were sources of

laughter, joy, and support. Special thanks to Ramazan and Sam whom I shared a house for

the last two years. How can I forget our long chats, discussions and the nights with

unstoppable and continuous laughs! Cuneyt, you are also a very special friend for me. We

shared a lot during these four years and I always considered you as a trustful person and a

real friend. We talked about many things that we cannot tell to the others, and I have to say

you always surprised me with your way of thinking. I warmly thank Maite for not just

being an excellent colleague but also being a very good friend. I am still missing your

friendship here and our joyful moments. Ruth, there are lots of things that make you special

xii

for me. You are a wonderful and a generous friend. I think you are the one who understood

me most and you are the person whom I shared most. I am sure we will stay as friends for

the rest of our lives. Thank you for just being yourself!

I also would like to thank to my oldest friends who are always with my during my entire

life: Aynur, Ceyhun and Baris. Sizler olmasaydiniz herhalde hayatimda cok buyuk bir

bosluk olurdu. Uzun yillardir dostlugumuz, paylastiklarimiz ve unutulmaz anilarimiz bizi

kardesten daha yakin bir hale getirdi. Sizler benim her zaman en buyuk sirdasim ve herseyi

en yalin halinde paylasabildigim nadir insanlar oldunuz. Hersey icin cok ama cok

tesekkurler.

I owe my loving thanks to my family, especially to my mother. Without their

encouragement and understanding it would have been impossible for me to finish this work.

The support of my family means a lot to me in all my life. Annecim, babacim ve canim

kardesim, sizler hayattaki en onemli kisilersiniz. Sizlerin varligi, destegi ve sevgisi benim

icin degisilmez. Her zaman beni desteklediginiz ve de benim yanimda oldugunuz icin cok

tesekkurler.

And my love, I even cannot think where I would be if you were not with me throughout this

journey. You continuously supported me and showed me your love and care. Thank you

for being with me, for your support and for your unconditional love. You are my other half,

and I am so lucky to be with you.

Ertug Ercin

Enschede, July 2012

Summary

Managing the water footprint of humanity is something in which both governments and

businesses have a key role. The actual reduction of humanity's water footprint depends on

the combination of what governments, businesses and consumers do and how their different

actions reinforce (or counteract) one another. Therefore, we need improved understanding

of how water footprint reduction strategies by governments on the one hand and companies

on the other hand can reinforce or counteract each other in achieving actual reduction of

humanity's water footprint. The objective of this thesis is to understand how the water

footprint concept can be used as a tool to inform governments and businesses about

sustainable, efficient and equitable water use and allocation. This study alternately takes a

governmental and a corporate perspective, because both actors have a significant role in

mitigating the water footprint of humanity and there is a strong interaction between the

roles and responsibilities of both actors.

This thesis starts with assessments from business perspective. Chapters 2 and 3

present two applications of WF for companies. The thesis continues with WF applications

from governmental perspective. Chapter 4 shows an application of the WF at national level.

Chapter 5 analysis how WF scenarios can inform national policy making in the long term.

After governmental studies, the thesis explores to which extent we can draw lessons from

the carbon footprint case in terms of adoption of policy responses for governments and

companies (Chapter 6). It finishes with the assessment of how the WF can be used in

combination with other environmental footprint indicators in a context of exploring the

relation between economics and environmental pressure (Chapter 7). The main findings are

summarized below, following the chapter-setup of the thesis:

The water footprint of soy milk and soy burger and equivalent animal products: As all

human water use is ultimately linked to final consumption, it is interesting to know the

specific water consumption and pollution behind various consumer goods, particularly for

goods that are water-intensive, such as foodstuffs. The water footprint of 1 litre soy milk is

297 litres, of which 99.7% refers to the supply chain. The water footprint of a 150 g soy

burger is 158 litres, of which 99.9% refers to the supply chain. Although most companies

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focus on just their own operational performance, this study shows that it is important to

consider the complete supply chain. The major part of the total water footprint stems from

ingredients that are based on agricultural products. In the case of soy milk, 62% of the total

water footprint is due to the soybean content in the product; in the case of soy burger, this is

74%. Thus, a detailed assessment of soybean cultivation is essential to understand the claim

that each product makes on freshwater resources. This study shows that shifting from non-

organic to organic farming can reduce the grey water footprint related to soybean

cultivation by 98%.Cow’s milk and beef burger have much larger water footprints than

their soy equivalents. The global average water footprint of a 150 g beef burger is 2350

litres and the water footprint of 1 litre of cow’s milk is 1050 litres.

The water footprint of a sugar-containing carbonated beverage: The water footprint of

the beverage studied has a water footprint of 150 to 300 litres of water per 0.5 litre bottle,

of which 99.7-99.8% refers to the supply chain. The results of this study show the

importance of a detailed supply-chain assessment in water footprint accounting. The study

shows that the water footprint of a beverage product is very sensitive to the production

locations of the agricultural inputs. Even though the amount of sugar is kept constant, the

water footprint of our product significantly changes according to the type of sugar input and

production location of the sugar. Additionally, the type of water footprint (green, blue and

grey) changes according to location, which are mainly driven by the difference in climatic

conditions and agricultural practice in the production locations. These results reveal the

importance of the spatial dimension of water footprint accounting. It shows that even small

ingredients can significantly affect the total water footprint of a product. On the other hand,

the study also shows that many of the components studied hardly contribute to the overall

water footprint. This is the first study quantifying the overhead water footprint of a product.

Strictly spoken, this component is part of the overall water footprint of a product, but it was

unclear how relevant it was. This study reveals that the overhead component is not

important for this kind of studies and is negligible in practice.

The water footprint of France: The total water footprint of production and consumption

in France is 90 billion m3/year and 106 billion m3/year respectively. The blue water

footprint of production is dominated by maize production. The basins of the Loire, Seine,

Garonne, and Escaut have been identified as priority basins where maize and industrial

production are the dominant factors for the blue water scarcity. About 47% of the water

footprint of French consumption is external and related to imported agricultural products.

Cotton, sugar cane and rice are the three major crops with the largest share in France’s

external blue water footprint of consumption and identified as critical products in a number

of severely water-scarce river basins. The basins of the Aral Sea and the Indus, Ganges,

Guadalquivir, Guadiana, Tigris & Euphrates, Ebro, Mississippi and Murray rivers are some

of the basins that have been identified as priority basins regarding the external blue water

footprint of French consumption. The study shows that analysis of the external water

footprint of a nation is necessary to get a complete picture of the relation between national

consumption and the use of water resources. It provides understanding of how national

consumption impacts on water resources elsewhere in the world.

Water footprint scenarios for 2050: This study develops water footprint scenarios for

2050 based on a number of drivers of change: population growth, economic growth,

production/trade pattern, consumption pattern (dietary change, bioenergy use) and

technological development. Our study comprises two assessments: one for the globe as a

whole, distinguishing between 16 world regions, and another one for Europe, whereby we

zoom in to the country level. This study shows how different driver will change the level of

water consumption and pollution globally in 2050. These estimates can form an important

basis for a further assessment of how humanity can mitigate future freshwater scarcity. We

showed with this study that reducing humanity’s water footprint to sustainable levels is

possible even with increasing populations, provided that consumption patterns change. This

study can help to guide corrective policies at both national and international levels, and to

set priorities for the years ahead in order to achieve sustainable and equitable use of the

world’s fresh water resources.

Understanding carbon and water footprints: The carbon footprint has become a widely

used concept in society, despite the lack of scientifically accepted and universally adopted

guidelines. Different stakeholders use the term with loose definitions or metaphorically,

according to their liking. The water footprint is becoming popular as well, and there is

substantial risk that it goes the same route as carbon footprint. The aim of this study to

xvi

extract lessons that may help to reduce the risk of losing the strict definition and

interpretation of the water footprint by understanding the mechanisms behind the adoption

of carbon footprint. Reduction and offsetting mechanisms are applied and supported widely

in response to the increasing concern about global warming. However, the effective

reduction of humanity’s carbon footprint is seriously challenged because of two reasons.

The first is the absence of a unique definition of the carbon footprint, so that reduction

targets and statements about carbon neutrality are difficult to interpret, which leaves room

for making developments show better than they really are. The second problem is that

existing mechanisms for offsetting leave room for creating externalities and rebound

effects. In the case of the water footprint, the identification of how to respond is still under

question. The strategy of water offsetting will face the same problem as in the case of

carbon, but there is another one: water offsetting can only be effective if it takes place at the

specific locations and in the specific periods of time when the water footprint that is to be

offset takes place. It is argued that the weakness of offsetting in the case of carbon footprint

shows that applying both offsetting and neutrality in water footprint cannot be effective

solutions and ideas. A more effective tool is probably direct water footprint reduction

targets to be adopted by both governments and companies.

Integrating ecological, carbon and water footprint into a “footprint family” of

indicators: In recent years, attempts have been made to develop an integrated footprint

approach for the assessment of the environmental impacts of production and consumption.

In this chapter, we provide for the first time a definition of the “footprint family” as a suite

of indicators to track human pressure on the planet and under different angles. It builds on

the premise that no single indicator per se is able to comprehensively monitor human

impact on the environment, but indicators rather need to be used and interpreted jointly.

The paper concludes by defining the “footprint family” of indicators and outlining its

appropriate policy use for the European Union (EU). This study can be of high interest for

both policy makers and researchers in the field of ecological indicators, as it brings clarity

on most of the misconceptions and misunderstanding around footprint indicators, their

accounting frameworks, messages, and range of application.

1 Introduction

Water plays a key role on our planet. Access to sufficient freshwater with adequate quality

is a prerequisite for human societies and undisturbed natural water flows are essential for

the functioning of ecosystems that support life on Earth (Costanza and Daly, 1992).

Throughout history, the scale of man’s influence on the quality and quantity of freshwater

resources has grown. Today, human use of freshwater is so large that water scarcity and

competition over water among users has become clearly visible in many parts of the world.

Therefore, it is important to understand what the driving forces behind human’s demand for

water are.

At present, irrigated agriculture is responsible for about 70% of all freshwater

abstractions by humans (Bruinsma, 2003; Shiklomanov and Rodda, 2003; UNESCO, 2006;

Molden, 2007) while it is responsible for roughly 90% of worldwide consumptive use of

freshwater (Hoekstra and Mekonnen, 2012a). In addition to agriculture, industries and

households use substantial amounts of water and contribute significantly to water pollution

(WWAP, 2009). In many places, urban areas, industry, agriculture, and natural ecosystems

compete for freshwater (Rosegrant and Ringler, 1998; UNESCO, 2006; Anderson and

Rosendahl, 2007).

Water resources policies have traditionally focused on managing direct water

withdrawals by ‘water users’. However, it has been shown that this approach is limited.

Final consumers, retailers, traders and businesses as indirect water users have stayed out of

the scope of water policies. By neglecting the connection between these actors and water

consumption and pollution along their supply chains, one limits options for comprehensive

water governance (Hoekstra et al., 2011). As all human water consumption is ultimately

linked to final consumption, it is important to use indicators that make this connection

clear, thereby enabling the design of water policies targeted at sustainable and equitable

water use.

Since the Dublin Conference in 1992 (ICWE, 1992), there is consensus that the

river basin is the appropriate unit for analysing freshwater availability and use. However,

18/ Chapter 1. Introduction

today the idea of water being a local issue is changing. In our interconnected world, each

river basin is connected to producers, traders and consumers around the world. Therefore,

the use of water in a basin is influenced by trade patterns and can be affected by

consumption far beyond the basin’s borders (Hoekstra and Chapagain, 2008). It is

important to understand that not only local but also global forces have a significant

influence on the use of water and water scarcity level within a river basin.

The background of this thesis is that it is becoming increasingly important to put

freshwater issues in a global context. Local water depletion and pollution are often closely

tied to the structure of the global economy. With increasing trade between nations and

continents, water is more frequently used to produce export goods. International trade in

commodities implies long-distance transfers of water in virtual form, where virtual water is

understood as the volume of water that has been used to produce a commodity and that is

thus virtually embedded in it. Knowledge about the virtual-water flows entering and leaving

a country can cast a completely new light on the actual water scarcity of a country. A

second starting point of this thesis is that it becomes increasingly relevant to consider the

linkages between consumer goods and impacts on freshwater systems. This can improve

our understanding of the processes that drive changes imposed on freshwater systems and

help to develop policies of wise water governance.

1.1 The water footprint concept

Understanding the consequences of human appropriation of freshwater resources requires

an analysis of how much water is needed for human use versus how much is available,

where and when (Rijsberman, 2006; Hoekstra and Chapagain, 2007; Lopez-Gunn and

Ramón Llamas, 2008; Rosegrant et al., 2009). Uncovering the link between consumption

and water use is vital to formulate better water governance. The ‘water footprint’ concept

was primarily formulated in the research context, to study the hidden links between human

consumption and water use and between global trade and water resources management

(Hoekstra and Chapagain, 2007). The concept helps us understand the relationships

between production, consumption and trade patterns and water use and the global

dimension in good water governance.

19

The water footprint is an indicator of freshwater use that looks not only at direct

water use of a consumer or producer, but also at the indirect water use. The water footprint

can be regarded as a comprehensive indicator of freshwater resources appropriation, next to

the traditional and restricted measure of water withdrawal. The water footprint of a product

is the volume of freshwater used to produce the product, measured over the full supply

chain. It is a multi-dimensional indicator, showing water consumption volumes by source

and polluted volumes by type of pollution; all components of a total water footprint are

specified geographically and temporally. The blue water footprint refers to consumption of

blue water resources (surface and ground water) along the supply chain of a product.

‘Consumption’ refers to loss of water from the available ground-surface water body in a

catchment area. Losses occur when water evaporates, returns to another catchment area or

the sea or is incorporated into a product. The green water footprint refers to consumption of

green water resources (rainwater stored in the soil as soil moisture). The grey water

footprint refers to pollution and is defined as the volume of freshwater that is required to

assimilate the load of pollutants given natural background concentrations and existing

ambient water quality standards (Hoekstra et al., 2011).

The water footprint was developed as an analogy to the ecological footprint

concept. It was first introduced by Hoekstra in 2002 to provide a consumption-based

indicator of water use (Hoekstra, 2003). It is an indicator of freshwater use that shows

direct and indirect water use of a producer or consumer. The first assessment of national

water footprints was carried out by Hoekstra and Hung ( 2002). A more extended

assessment was done by Hoekstra and Chapagain (2007; 2008) and a third, even more

detailed, assessment was done by Hoekstra and Mekonnen (2012a).

1.2 Water footprint assessment

Water footprint assessment is an analytical tool; it can be instrumental in helping to

understand how activities and products relate to water scarcity and pollution and related

impacts and what can be done to make sure that activities and products do not contribute to

unsustainable use of freshwater. As a tool, water footprint assessment provides insight; it

does not tell ‘what to do’. Rather it helps to understand what can be done.


Water footprint assessment refers to the full range of activities to (i) quantify and

locate the water footprint of a process, product, producer or consumer or to quantify in

space and time the water footprint in a specified geographic area, (ii) assess the

environmental, social and economic sustainability of this water footprint and (iii) formulate

a response strategy (Hoekstra et al., 2011). Broadly speaking, the goal of assessing water

footprints is to analyse how human activities or specific products relate to issues of water

scarcity and pollution and to see how activities and products can become more sustainable

from a water perspective.

1.3 Problem statement

Managing the water footprint of humanity is something in which both governments and

businesses have a key role. The actual reduction of humanity's water footprint depends on

the combination of what governments, businesses and consumers do and how their different

actions reinforce (or counteract) one another. Therefore, we need improved understanding

of how water footprint reduction strategies by governments on the one hand and companies

on the other hand can reinforce or counteract each other in achieving actual reduction of

humanity's water footprint.

1.3.1 Business perspective

Water is crucial for the economy. Virtually every economic sector, from agriculture,

electric power, manufacturing, beverage and apparel to tourism, relies on freshwater to

sustain its business. Yet water is becoming scarcer globally and every indication is that it

will become even more so in the future. Decreasing availability, declining quality, and

growing demand for water are creating significant challenges to businesses and investors

who have traditionally taken clean, reliable and inexpensive water for granted. These

problems are already causing decreases in companies’ water allotments, shifts toward full-

cost water pricing, more stringent water quality regulations, growing community

opposition, and increased public scrutiny of corporate water practices.

For many companies, freshwater is a basic ingredient for their operations, while

effluents may pollute the local ecosystem. Various companies have addressed these issues

21

and formulated proactive management strategies (Gerbens-Leenes et al., 2003). Failure to

manage the freshwater issue raises four serious risks for a company: damage to the

corporate image, the threat of increased regulatory control, financial risks caused by

pollution, and insufficient freshwater availability for business operations (Rondinelli and

Berry, 2000; WWF, 2007). Therefore, the efficient use of freshwater and control of

pollution is often part of sustainability issues addressed by business. However, how to

address the sustainability of the full supply chain of products from a freshwater point of

view is still an open question to most companies.

1.3.2 Governmental perspective

Recently, it has become evident that the water problems of a country can no longer be

solved by the traditional ‘production perspective’ alone. Due to the globalised structure of

trade, the ‘real’ consumers of the water resources are often not the victims of the impacts

caused by their consumption. Traditionally, national governments do not consider the

virtual water flows through imports in their national water policies and exclude water use

and its impacts outside their country to support national consumption. In order to support a

broader sort of analysis and better inform decision-making, this traditional way of thinking

in national water policy should be extended. A responsible and fair water policy would

hence have to have an international component (Hoekstra et al., 2011).

Traditional national water use accounts only refer to the water withdrawals for

various sectors within a country. They do not distinguish between water use for making

products for domestic consumption and water use for producing export products. They also

exclude data on water use outside the country to support national consumption. In order to

support a broader sort of analysis and better inform decision making, national water use

accounts need to be extended. How to do this, and how to use those accounts in informing

the national policy, is an unanswered question for governments, that have just started to

become aware of the international dimension of good water governance.


1.3.3 Need for set of indicators

Solving the sustainability challenge requires an approach that considers all aspects of

human pressure on the world’s natural resources. An integrated ecosystem approach is

required in order to tackle multiple issues concurrently, and help to avoid additional costs

that are created when taking measures that reduce one sort of pressure on the environment

but then appear to increase another sort of pressure. This can happen, for example, when

policies to reduce carbon footprint lead to an increase of the water footprint. Therefore, a

set of indicators is needed to account for the environmental consequences of human

activities. The water footprint is able to capture just one aspect of the full complexity of

sustainable development: human appropriation of freshwater resources. The water footprint

should be addressed with other footprint indicators (carbon and ecological footprints) in

order to more comprehensively monitor the environmental pillar of sustainability.

In addition, it can be useful to examine whether experiences with the way the

global society applies and responds to one environmental pressure indicator can provide

lessons for how we can effectively use and respond to another indicator.

1.4 Objective

The objective of this thesis is to understand how the water footprint concept can be used as

a tool to inform governments and businesses about sustainable, efficient and equitable

water use and allocation. This study alternately takes a governmental and a corporate

perspective, because both actors have a significant role in mitigating the water footprint of

humanity and there is a strong interaction between the roles and responsibilities of both

actors.

From the business perspective, this thesis aims to apply and elaborate existing

methods for business water footprint accounting and water footprint sustainability

assessment, and to explore how the water footprint concept can form a basis for companies

to extend their current corporate water strategies to the next steps to be taken: product

transparency and water footprint reduction in the supply chain. The aim is to develop

understanding of how a company can measure water consumption and improve water

23

management across its operations and supply chain, with the final goal of building a

sustainable water strategy.

With respect to the governmental perspective, the thesis explores whether the

framework of water footprint and virtual water trade assessment can contribute to the

identification of national water policy measures alternative or in addition to the traditional

ones, which are limited to measures that focus either on increasing national water supply or

on lowering water demand within the national territory by increasing water use efficiency.

1.5 Research questions

To guide this study the following research questions have been formulated:

(i) How can water footprint assessment be applied to business water accounting and how

can companies benefit from the water footprint concept to build a wise corporate water

strategy?

(ii) How can the water footprint concept be applied by national governments in

formulating governmental policy that contributes to sustainable water use?

(iii) Can we learn from experiences with the use of the carbon footprint concept by business

and governments in order to effectively use the water footprint concept? Are the sorts

of policy instruments established for the carbon footprint applicable to the water

footprint?

(iv) How can water footprint be addressed with other environmental footprints in a single

analytical framework?

1.6 Structure of the thesis

This thesis starts with assessments from business perspective. Chapters 2 and 3 present two

applications of WF for companies. The thesis continues with WF applications from

governmental perspective. Chapter 4 shows an application of the WF at national level.

Chapter 5 analysis how WF scenarios can inform national policy making in the long term.

After governmental studies, the thesis explores to which extent we can draw lessons from

the carbon footprint case in terms of adoption of policy responses for governments and


companies (Chapter 6). It finishes with the assessment of how the WF can be used in

combination with other environmental footprint indicators in a context of exploring the

relation between economics and environmental pressure (Chapter 7).

Chapter 2 shows an application of water footprint accounting in business water

accounting on product level. It analyses the water footprint of soymilk and soy burger

products and compares them with their equivalent animal-based products (cow’s milk and

beef burgers). It starts with the assessment of the water footprint of soybean cultivation,

differentiating between the green, blue and grey water components. Different types of

soybean production systems are analysed: organic vs. non-organic and irrigated vs. rainfed.

Next, the water footprint of the final products is assessed in relation to the composition of

the product and the characteristics of the process and producing facility. Finally, it

compares the water footprint of soy products with equivalent animal products.

The third chapter includes a pilot study on water footprint accounting and impact

assessment for a hypothetical sugar-containing carbonated beverage. The water footprint of

the beverage has been calculated by quantifying the water footprint of each input separately

and by accounting for process water use as well. In addition, a local environmental impact

assessment has been carried out, by looking at the occurrence of environmental problems in

the regions where the water footprint of the product is located.

Chapter 4 carries out a water footprint assessment for France from both a

production and consumption perspective. It analyses how French water resources are

allocated over various purposes, and examines where the water footprint of production

within France violates local environmental flow requirements and ambient water quality

standards. In addition, it quantifies which volumes of French water resources are allocated

for making products for export and to assess the impact related to this water footprint for

export. It also includes the analysis of the external water footprint of French consumption,

to get a complete picture of how national consumption translates to water use, not only in

France, but also abroad. In this way we show French dependency on external water

resources and the sustainability of imports.

25

Chapter 5 develops water footprint scenarios for 2050 based on a number of

drivers of change: population growth, economic growth, production/trade pattern,

consumption pattern (dietary change, bioenergy use), technological development. It goes

beyond the previous global water scenario studies by a combination of factors: (i) it

addresses blue and green water consumption instead of blue water withdrawal volumes; (ii)

it considers water pollution in terms of grey water footprint; (iii) it analyses agricultural,

domestic as well as industrial water consumption; (iv) it disaggregates consumption along

major commodity groups; (v) it integrates all major critical drivers of change under a

single, consistent framework. The water footprint scenarios consist of two assessments: one

for the globe as a whole, distinguishing between 16 world regions, and another one for

Europe, whereby we zoom in to the country level. Global study analyses the changes in the

water footprint of production and consumption for possible futures by region and to

elaborate the main drivers of this change. In addition, it assesses virtual water flows

between the regions of the world to show dependencies of the regions on water resources in

the other regions under different possible futures. In the European case study, we assess the

water footprint of production and consumption at country level and Europe’s dependence

on water resources elsewhere in the world.

Chapter 6 analyses the origins and the characteristics of the carbon and water

footprints in order to understand their similarities and differences and to derive lessons on

how society and business can adequately build on the two concepts. The two concepts are

compared from a methodological point of view. We discuss response mechanisms that have

been developed, with the hope that experiences in one field might be able to benefit the

other. We address the question whether policy responses that have been developed for the

carbon footprint are applicable and suitable to the water footprint and investigate

meaningful policy responses for the water footprint. Finally, we elaborate the role of

governments and businesses in managing the footprints and formulating policy options.

Chapter 7 reflects on the role of water footprint in broader environmental policy

development. It discusses the “footprint family” as a suite of indicators to track human

pressure on the planet and under different angles. A description of the research question,

rationale and methodology of the Ecological, Carbon and Water Footprint is first provided.


Similarities and differences among the three indicators are then highlighted to show how

these indicators overlap and complement each other. It concludes by defining the “footprint

family” of indicators and outlining its appropriate policy use at EU level.

The last chapter concludes the thesis by putting the main findings in the previous

chapters into perspective.

2 The water footprint of soy milk and soy burger and equivalent animal products1

Abstract

As all human water use is ultimately linked to final consumption, it is interesting to know

the specific water consumption and pollution behind various consumer goods, particularly

for goods that are water-intensive, such as foodstuffs. The objective of this study is to

quantify the water footprints of soy milk and soy burger and compare them with the water

footprints of equivalent animal products (cow’s milk and beef burger). The study focuses

on the assessment of the water footprint of soy milk produced in a specific factory in

Belgium and soy burger produced in another factory in the Netherlands. The ingredients

used in the products are same as real products and taken from real case studies. We

analysed organic and non-organic soybean farms in three different countries from where the

soybeans are imported (Canada, China, and France). Organic production reduces soil

evaporation and diminishes the grey water footprint, ultimately reducing the total water

footprint. The water footprint of 1 litre soy milk is 297 litres, of which 99.7% refers to the

supply chain. The water footprint of a 150 g soy burger is 158 litres, of which 99.9% refers

to the supply chain. Although most companies focus on just their own operational

performance, this study shows that it is important to consider the complete supply chain.

The major part of the total water footprint stems from ingredients that are based on

agricultural products. In the case of soy milk, 62% of the total water footprint is due to the

soybean content in the product; in the case of soy burger, this is 74%. Thus, a detailed

assessment of soybean cultivation is essential to understand the claim that each product

makes on freshwater resources. This study shows that shifting from non-organic to organic

farming can reduce the grey water footprint related to soybean cultivation by 98%.Cow’s

milk and beef burger have much larger water footprints than their soy equivalents. The

global average water footprint of a 150 g beef burger is 2350 litres and the water footprint

of 1 litre of cow’s milk is 1050 litres.

1 Based on Ercin et al. (2012a)

28/ Chapter 2. The water footprint of soy milk and soy burger

2.1 Introduction

Given that severe freshwater scarcity is a common phenomenon in many regions of the

world, improving the governance of the world’s limited annual freshwater supply is a major

challenge, not only relevant to water users and managers but also to final consumers,

businesses and policymakers in a more general sense (UNESCO, 2006). About 86% of all

water used in the world is to grow food (Hoekstra and Chapagain, 2008). Therefore, food

choices can have a big impact on water demand (Steinfeld et al., 2006; De Fraiture et al.,

2007; Peden et al., 2007; Galloway et al., 2007). In industrialised countries, an average

meat-eater consumes the equivalent of about 3,600 litres of water a day, which is 1.6 times

more than the 2,300 litres used daily by people on vegetarian diets (assuming the

vegetarians still consume dairy products; Hoekstra, 2010).

Fresh water is a basic ingredient in the operations and supply chains of many

companies. A company may face multiple risks related to failure in properly managing

freshwater supplies: damage to its corporate image, the threat of increased regulatory

control, financial risks caused by pollution, and inadequate freshwater availability for

business operations (Rondinelli and Berry, 2000; Pegram et al., 2009). The need for the

food industry to take a responsible approach towards the sustainable use and conservation

of fresh water is therefore vital.

The ‘water footprint’ is an indicator of water use that looks at both direct and

indirect water use by a consumer or producer (Hoekstra, 2003). The water footprint is a

comprehensive indicator of freshwater resources appropriation, which goes beyond

traditional restrictive measures of water withdrawal. The water footprint of a product is

defined as the total volume of fresh water that is used directly or indirectly to produce the

product. It is estimated by considering water consumption and pollution in all steps of the

production chain. (Hoekstra et al., 2011). The blue water footprint refers to consumption of

blue water resources (surface and ground water) along the supply chain of a product.

‘Consumption’ refers to the loss of water from the available ground and surface water in a

given catchment area. It includes evaporatranspiration, water incorporated into products

and return waters to another catchment area or the sea. The green water footprint refers to

29

consumption of green water resources (rainwater). The grey water footprint refers to

pollution and is defined as the volume of freshwater that is required to assimilate the load

of pollutants based on existing ambient water quality standards.

This paper analyses the water footprints of soy milk and soy burger and compares

them with the water footprints of two equivalent animal products (cow’s milk and beef

burger). For this purpose, we first identified the production-chain diagram of 1 litre of soy

milk and of 150 g soy burger. We also indicated the relevant process steps with substantial

water footprints. The study focuses on the assessment of the water footprint of soy milk

produced in a specific factory in Belgium and soy burger produced in a specific factory in

the Netherlands. The soybeans used in the manufacturing of the soy products in these two

countries are imported. The study starts with the assessment of the water footprint of

soybean cultivation in Canada, China and France, three of the actual source countries,

differentiating between the green, blue and grey water footprint components. Different

types of soybean production systems are analysed: organic versus non-organic and irrigated

versus rain-fed. Next, the water footprint of each of the final products is assessed based on

the composition of the product and the characteristics of the production process and

producing facility. Finally, we compare the water footprints of soy products with the water

footprints of equivalent animal products.

2.2 Method and data

In order to estimate the water footprint of soy milk and soy burger, first we identified

production systems. A production system consists of sequential process steps. Figures 2.1

and 2.2 show the production system of soy milk and soy burger, respectively. These

production diagrams show only the major process steps during the production and the

inputs for each step that are most relevant for water footprint accounting. They do not

show other steps in the life cycle of the products like transportation, elevation, distribution,

end-use and disposal.

Taking the perspective of the producer of the soy milk and soy burger, the water

footprints of the soy products include an operational and a supply-chain water footprint.

The operational (or direct) water footprint is the volume of freshwater consumed or

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Table 2.1. Water footprints of raw materials and process water footprints for the ingredients and other components of 1 litre soy milk.

a Mekonnen and Hoekstra (2010a); Van Oel and Hoekstra (2010) for wood. b Van der Leeden et al. (1990) c Data for soybean: own calculations. d FDA (2006) *Total weight of ingredients is 0.1 kg. The rest of the weight is water, which is added in the operational phase.

1 litre of

soy milk Raw material

Amount*

(g) Source

Water footprint of raw material

(m3/ton)a

Process water footprint (m3/ton)b

Fractions for products useda

Green Blue Grey Green Blue Grey Product fraction

Value fraction

Ingredients

Soybeanc Soybean 70 Canada + China (organic)

1753.5 0 18.5 0 0 0 0.64 0.95

Cane sugar

Sugar cane 25 Cuba 358 50 2 0 0 0 0.11 0.87

Maize starch Maize 0.30 China 565 12 335 0 0 0 0.75 1

Vanilla flavour Vanilla 0.15 USA 67269 7790 0 0 0 0 9d 1

Other components

Cardboard Wood 25 Germany 616 0 0 0 0 180 1 1

Cap Oil 2

Sweden (raw) - Germany (process)

0 0 10 0 0 225 1 1

Tray - cardboard Wood 10 Germany 616 0 0 0 0 180 1 1

Stretch film (LDPE)

Oil 1.5


0 0 10 0 0 225 1 1

33

Table 2.2. Water footprints of raw materials and process water footprints for the ingredients and other components of 150 g soy burger.

150 g of soy burger

Raw material

Amount* (g) Source

Water footprint of raw material

(m3/ton)a

Process water footprint (m3/ton)b

Fractions for products useda


Value fraction

Ingredients

Soybeanc Soybean 25

France + Canada (non-organic)

1860 130 795 0 0 0 0.64 0.95

Maize Maize 4 Turkey 646 208 277 0 0 0 1 1

Soy milk powder Soybean 4 USA 1560 92 10 0 0 0 0.57 1

Soya paste Soybean 4 USA 1560 92 10 0 0 0 3.75 1

Onions Onions 4 Nether-lands 68 5 18 0 0 0 1 1

Paprika green

Peppers green 5 Spain 39 3 37 0 0 0 1 1

Carrots Carrots 2 Nether-lands 57 3 18 0 0 0 1 1

Other components

Sleeve (cardboard) Wood 15 Germany 616 0 0 0 0 180 1 1

Plastic cup Oil 15


0 0 10 0 0 225 1 1

Cardboard box (contains 6 burger packs)

Wood 25 Germany 616 0 0 0 0 180 1 1

Stretch film (LDPE) Oil 0.5


0 0 10 0 0 225 1 1

a Mekonnen and Hoekstra (2010a); Van Oel and Hoekstra (2010) for wood. b Van der Leeden et al. (1990) c Data for soybean: own calculations. *Total weight of ingredients is 0.05 kg. The rest of the weight is water, which is added in the operational phase.


The water footprints of different ingredients and other inputs are calculated

distinguishing between the green, blue and grey water footprint components. The water

footprint definitions and calculation methods applied follow Hoekstra et al. (2011). In order

to calculate the water footprint of the soy products, we first calculated the water footprints

of the original materials (raw materials) of the ingredients. The water footprint of

agricultural raw materials (crops) is calculated as follows:

The green and blue component in water footprint of crops (WFgreen/blue, m3/ton) is

calculated as the green and blue components in crop water use (CWUgreen/blue, m3/ha)

divided by the crop yield (Y, ton/ha).

WFgreen/blue / (2.1)

The green and blue water crop water use were calculated using the CROPWAT

model (Allen et al., 1998; FAO, 2009a). Within the CROPWAT model, the ‘irrigation

schedule option’ was applied, which includes dynamic soil water balance and tracks the soil

moisture content over time (Allen et al., 1998). The calculations were done using climate

data from the nearest and most representative meteorological stations and a specific

cropping pattern for each crop according to the type of climate. Monthly values of major

climatic parameters were obtained from the CLIMWAT database (FAO, 2009b). Crop area

data were taken from Monfreda et al. (2008); crop parameters (crop coefficients, planting

date and harvesting date) were taken from Allen et al. (1998) and FAO (2009a). Types of

soil and average crop yield data were obtained from the farms (Table 2.3). Soil information

was taken from FAO (2009a). All collected data are used as inputs for the CROPWAT

model for calculation of crop water use.

In the case of the Chinese organic soybean production, organic compost mixed

with the straw of the crop and the waste of livestock was applied. 50% of the soil surface

was assumed to be covered by the organic crop residue mulch, with the soil evaporation

being reduced by about 25% (Allen et al., 1998). For the crop coefficients in the different

growth stages this means: Kc,ini, which represents mostly evaporation from soil, is reduced

35

by about 25%; Kc,mid is reduced by 25% of the difference between the single crop

coefficient (Kc,mid) and the basal crop coefficient (Kcb,mid); and Kc,end is similarly

reduced by 25% of the difference between the single crop coefficient (Kc,end) and the basal

crop coefficient (Kcb,end). Generally, the differences between the Kc and Kcb values are

only 5-10%, so that the adjustment to Kc,mid and Kc,end to account for organic mulch may

not be very large.

Table 2.3. Planting and harvesting dates, yield and type of soil for the five soybean farms considered.

Crop Planting date*

Harvesting date*

Yield (ton/ha)* Type of soil

Canada organic rainfed 15 May 11 October 2.4 Sandy loam - Clay loam

Canada non-organic rainfed 15 May 11 October 2.5 Clay loam

China organic rainfed 15 May 11 October 2.9 Brown soil

France non-organic rainfed 15 May 11 October 1.9 Calcareous clay

France non-organic irrigated 15 May 11 October 3.1 Calcareous clay

* Farm data

The grey water footprint of crops is calculated by dividing the pollutant load (L, in

mass/time) by the difference between the ambient water quality standard for that pollutant

(the maximum acceptable concentration cmax, in mass/volume) and its natural

concentration in the receiving water body (cnat, in mass/volume).

, proc greymax nat

LWFc c

=−

(2.2)


Table 2.4. Fertilizer and pesticide application and leaching rate and ambient water quality standards.

Type of agriculture

Chemical Active substance

Application rate (kg/ha)

Leaching rate (%)

Leaching rate source

Standard(mg/l)

Standard source

Organic Sulphate of potash sulphate 25 60

Eriksen and Askegaard (2000)

300 MDEQ (1996) in MacDonald et al. (1999)

Rock phosphate phosphorus 11 0

Organic compost nitrogen 1 10

Hoekstra and Chapagain (2008)

10 EPA (2010)

Potassium chloride chloride 20 85 Stites and

Kraft (2001) 860

EPA (2010) CMC - Criteria Maximum Concentration

Non-organic TSP phosphorus 24 0

Touchdown glyphosate 1 0.01 Dousset et al. (2004) 0.065

MDEQ (1996) in MacDonald et al. (1999)

Boundary metolachlor 2.4 1 Singh (2003) 0.008 MDEQ (1996) in MacDonald et al. (1999)

Boundary metribuzin 2.4 0 Kjaer et al. (2005) 0.001


P2O5 phosphorus 33 0

Lasso alachlor 2 2.5 Persicani et al. (1995) 0.048


Generally, soybean production leads to more than one form of pollution. The grey

water footprint was estimated separately for each pollutant and finally determined by the

pollutant that appeared to be most critical, i.e. the one that is associated with the largest

pollutant-specific grey water footprint (if there is enough water to assimilate this pollutant,

all other pollutants have been assimilated as well). The total volume of water required per

ton of pollutant was calculated by considering the volume of pollutant leached (ton/ton) and

the maximum allowable concentration in the ambient water system. The natural

37

concentration of pollutants in the receiving water body was assumed to be negligible.

Pollutant-specific leaching fractions and ambient water quality standards were taken from

the literature (Table 2.4). In the case of phosphorus, good estimates on the fractions that

reach the water bodies by leaching or runoff are very difficult to obtain. The problem for a

substance like phosphorus (P) is that it partly accumulates in the soil, so that not all P that is

not taken up by the plant immediately reaches the groundwater, but on the other hand may

do so later. In this study we assumed a P leaching rate of zero.

Second step in calculation of water footprints of the soy products is the calculation

of water footprints of each ingredient of the soy products. The water footprint of ingredient

(p) is calculated as:

,

(2.3)

in which WFprod[p] is the water footprint of ingredient, WFprod[i] is the water footprint of

raw material i and WFproc[p] the process water footprint. Parameter fp[p,i] is the ‘product

fraction’ and parameter fv[p] is the ‘value fraction’. The product fraction of ingredient p

that is processed from raw material i (fp[p,i], mass/mass) is defined as the quantity of the

ingredient (w[p], mass) obtained per quantity of raw material (w[i], mass):

][][],[

iwpwipf p = (2.4)

The value fraction of ingredient p (fv[p], monetary unit/monetary unit) is defined

as the ratio of the market value of this ingredient to the aggregated market value of all the

outputs products (p=1 to z) obtained from the raw material:

( )1

[ ] [ ][ ][ ] [ ]

v z

p

price p w pf pprice p w p

=

×=

×∑

(2.5)


The water footprint of the soy products is then sum of water footprint of all

ingredients (p=1 to y) multiplied with the amounts of ingredients (Mp) used in the products:

x (2.6)

As an example to water footprint calculation of the ingredients, we show here the

case of soybean used in 150 g of soy burger. The amount of soybean used in the soy burger

is 0.025 kg and is cultivated in Canada and France (50% each). The green, blue and grey

water footprints of the soybean mix are 1860, 130 and 795 m3/ton, respectively. About 86%

of the weight of soybean becomes dehulled soybean (DS) and about 74% of the DS weight

becomes base milk. The product fraction for soybean in the product (basemilk) is thus 0.86

× 0.74 = 0.64. In the process from soybean to basemilk, there are also by-products with

some value. The value of the basemilk is 94% of the aggregated value of soybean products.

Therefore, 94% of the water footprint of the soybean is attributed to basemilk. The water

footprint of the basemilk as used in the soy milk is calculated by multiplying the water

footprint of soybean by the value fraction and amount used and dividing by the product

fraction. The green water footprint of the basemilk is thus: (1860×0.94×0.025)/0.64 = 69.1

litres. The blue water footprint: (130×0.94×0.025)/0.64 = 4.8 litres. The grey water

footprint: (795×0.94×0.025)/0.64 = 29.5 litres.

For the other agricultural ingredients, water footprints of raw products, product

fractions and value fractions have been taken from Mekonnen and Hoekstra (2010a). We

calculated the product and value fractions of the vanilla extract according to the extracting

process defined as in FDA (2006). In this calculation, we assumed that single fold vanilla

extract is used in the soy milk. The water footprints of raw materials, process water

footprints, product fractions and value fractions, on which the soy milk and soy burger

water footprint's calculation is based, are given in Table 2.1 and 2.2.

The supply-chain water footprint of soy products is not only caused by ingredients

but also other components integral to the whole product. These include closure, labelling

and packaging materials. The process water footprints and the water footprints associated

39

with other raw materials used (oil, PE, LDPE, PP) have been derived from Van der Leeden

et al. (1990). The detailed list of other components of the supply-chain water footprint of

the product is given in Table 2.1 and 2.2. The water footprints of raw materials, process

water footprints, product fractions and value fraction are presented in Table 2.1 and 2.2.

Data related to the operational water footprint of soy milk and soy burger are taken

from two real factories in Belgium and the Netherlands. Both factories have treatment

plants that treat the wastewater before discharging it into the receiving water bodies. All

wastewater leaving the factories is treated with 100% treatment performance at both

treatment plants and effluent characteristics of the treated wastewater are below the legal

limits. Therefore, we took the grey water footprint as zero by assuming that the

concentration of the pollutant in the effluent is equal to its actual concentration in the

receiving water body.

The water used as an ingredient is equal to 0.1 litres per 150 g of soy burger and

0.9 litres per 1 litre of soy milk. The production of soy milk and soy burger includes the

following process steps: base milk preparation, mixing, filling, labelling and packaging.

The water balance recordings of the factories showed that the amount of water lost

(evaporated) is zero during all these processes. Base milk in the production process refers to

the preparation of concentrated milk.

The water footprints of cow’s milk and beef depend on the water footprints of the

feed ingredients consumed by the animal during its lifetime and the water footprints related

to drinking and service water (Hoekstra and Chapagain, 2008). Clearly, one needs to know

the age of the animal when slaughtered and the diet of the animal during the various stages

of its life. The water footprints of cow’s milk and beef burger have been taken from

Mekonnen and Hoekstra (2010b). For the comparison with the soy products, the water

footprint of packaging is included in the water footprints of cow’s milk and beef burger as

well.


0 500 1000 1500 2000 2500 3000 3500

China (organic)

Canada (organic)

France (non-organic irrigated)

France (non-organic rainfed)

Canada (non-organic)

m3/ton

Green WF

Blue WF

Grey WF

2.3 Results

2.3.1 Water footprint of soybean

The water footprints of soybean cultivated in five different farms located in three

different countries are shown in Figure 2.3. The soybean from the Canadian non-organic

farm has the largest water footprint, followed by the two French non-organic farms, the

Canadian organic farm and Chinese organic farm. The blue water footprint component is

zero except for the soybean from the French irrigated farm. The soybean from the rest of

the farms is rainfed. The largest grey water footprint is found for the soybean from the

Canadian non–organic farm.

Figure 2.3. The water footprint of soybeans (as primary crops) from different farms (m3/ton).

Soybean cultivation in Canada

In Canada, two different plantations were analysed: a rainfed organic and a rainfed

non-organic soybean farm. As reported in Table 2.3, crop yields for the organic and non-

organic soybean production in the Canadian farms are similar (2.4 and 2.5 ton/ha,

respectively). The water footprint of non-organic soybean production is about 3172 m3/ton

(2069 m3/ton green and 1103 m3/ton grey) (Figure 2.3). The grey water footprint is

determined by Boundary herbicide, which has the largest pollutant-specific grey water

footprint (1103 m3/ton), followed by potassium chloride (8 m3/ton), Touchdown (1 m3/ton)

41

and TSP (0 m3/ton). The total water footprint of organic soybean production in the

Canadian farm is around 2024 m3/ton (2004 m3/ton green, 20 m3/ton grey). In this case, the

sulphate of potash is the most critical pollutant (20 m3/ton). The nitrogen fertilization

through symbiotic and endophytic bacteria as applied in organic farming has a zero grey

water footprint.

Soybean cultivation in China

The Chinese organic rainfed farm under study achieves high yields, amounting to

about 2.9 ton/ha, notably higher than the Chinese national average (1.7 ton/ha). The total

water footprint of the Chinese organic rainfed soybean production is 1520 m3/ton (1503

m3/ton green and 17 m3/ton grey). The grey water footprint is related to the sulphate

pollution coming from the sulphate of potash applied. The grey water footprint of nitrogen

due to organic compost is 4 m3/ton and the one of phosphorus (P2O5) is negligible. In this

case, organic compost mixed by the straw of the crop and the waste of the livestock is

applied, mainly before planting.

Soybean cultivation in France

The non-organic rainfed French farm studied has a low yield of around 1.9 ton/ha,

whereas the irrigated one gives 3.1 ton/ha, higher than the national average (2.5 ton/ha).

The water footprint of the soybean from the rainfed farm is calculated as 2651 m3/ton (2048

m3/ton green and 603 m3/ton grey). The water footprint for the irrigated farm is estimated

as 2145 m3/ton (1255 m3/ton green, 519 m3/ton blue and 370 m3/ton grey). In both cases,

the grey water footprint is determined by the Lasso pesticide (alachlor) applied (603 and

370 m3/ton for rainfed and irrigated production, respectively), followed by the potassium

chloride pollution (10 and 6 m3/ton respectively) and TSP (0 m3/ton).

2.3.2 Water footprint of soy products

The operational water footprints of soy milk and soy burger are very small (Tables 2.5 and

2.6). Both green and grey water footprints are zero. The blue water footprint is 0.9 litre of

water for soy milk and 0.1 litres for soy burger. The total operational water footprint is thus

no more than the water used as ingredient of the products.


The water footprints of the two soy products are largely determined by the supply

chain components. About 62% of the total water footprint of soy milk refers to the water

footprint of soybean cultivation. In the case of soy burger, this is 65%. In the case of soy

milk, 90% of the supply-chain water footprint is from ingredients (mainly soybean and cane

sugar) and 10% is from other components (mainly cardboard). For soy burger, the

percentages are 78% and 22% respectively.

Table 2.5. The water footprint of 1 litre of soy milk.

Water footprint (litres)

Green Blue Grey Total % in total

Water incorporated into the soy milk 0 0.9 0 0.9 0.3

Water consumed during process 0 0 0 0 0

Wastewater discharge 0 0 0 0 0

Operational water footprint 0 0.9 0 0.9 0.3

Soybean (basemilk) 182.3 0 1.9 184.2 62

Cane sugar 71.1 9.9 0.4 81.5 27.5

Maize starch 0.2 0 0.1 0.4 0.1

Vanilla flavour 1.1 0.1 0 1.3 0.4

Ingredients total 254.7 10 2.4 267.4 90

Cardboard 15.4 0.0 4.5 19.9 6.7

Cap 0.0 0.0 0.5 0.5 0.2

Tray - cardboard 6.2 0.0 1.8 8.0 2.7

Stretch film (LDPE) 0.0 0.0 0.4 0.4 0.1

Other components total 21.6 0 7.2 28.8 9.7

Supply-chain water footprint 276.4 10.1 9.6 296 99.7

Total 276.4 11.0 9.6 296.9

The results in Tables 2.5 and 2.6 are calculated based on the figures given in Table

2.1 and 2.2. As an example, we show here the calculation of the water footprint of soybean

used in 150 g of soy burger. The amount of soybean used in the soy burger is 0.025 kg and

is cultivated in Canada and France (50% each). All soybeans come from non-organic farms.

In France, the soybean come partly from rainfed lands and partly from irrigated lands. The

43

Canadian soybean is taken from rainfed fields. The water footprints of soybeans as primary

crop from different locations are given in Table 2.7. The green, blue and grey water

footprints of soybean from Canada are 2069, 0 and 1103 m3/ton, respectively. For rainfed

soybean from France this is 2048, 0, and 603 m3/ton, respectively. For irrigated French

soybean, we find values of 1255, 519 and 370 m3/ton. Based on relative amounts per

source, we can calculate that the green, blue and grey water footprints of the resulting

soybean mix are 1860, 130 and 795 m3/ton, respectively.

Table 2.6. The water footprint of 150 g of soy burger.


Green Blue Grey Total % in total

Water incorporated into the soy milk 0 0.1 0 0.1 0.06

Water consumed during process 0 0 0 0 0

Wastewater discharge 0 0 0 0 0

Operational water footprint 0 0.1 0 0.1 0.06

Soybean (basemilk) 69.1 4.8 29.5 103.4 65.5

Maize 2.6 0.8 1.1 4.5 2.8

Soy milk powder 10.9 0.6 0.1 11.7 7.4

Soya paste 1.7 0.1 0.0 1.8 1.1

Onions 0.3 0 0.1 0.4 0.3

Paprika green 0.2 0 0.2 0.4 0.3

Carrots 0.1 0 0 0.2 0.1

Ingredients total 84.9 6.3 31 122.4 77.5

Sleeve (cardboard) 9.2 0 2.7 11.9 7.5

Plastic cup 0.0 0 3.5 3.5 2.2

Cardboard box (contains 6 burger packs) 15.4 0 4.5 19.9 2.6

Stretch film (LDPE) 0 0 0.1 0.1 0.06

Other components total 24.6 0 10.8 35.4 22.36

Supply-chain water footprint 109.5 6.4 41.8 157.8 99.9

Total 109.5 6.5 41.8 157.9


The total water footprints of 1 litre of soy milk and 150 g of soy burger are

calculated as 297 and 158 litres respectively. For soy milk, 99.7% of total water footprint

stems from the supply-chain water footprint. For soy burger this is 99.9%. This highlights

the importance of detailed supply chain assessments for both products and businesses.

Common practice in business water accounting is the focus on the operational water

consumption. However, this study shows that compared to the supply-chain water footprint,

the operational side is almost negligible. The diagrams in Figure 2.4 show the colour

composition of the water footprints of soy milk and soy burger. 93% of the total water

footprint of the 1 litre of soy milk is from green water resources, 4% is from blue water

resources and 3% is the grey water footprint component. The colours of the water footprint

of 150 g soy burger are 69% green, 4% blue and 27% the grey.

Table 2.7. Summary of the water footprints of soybeans as primary crop (as input to a soy burger).

Farm Water footprint (m3/ton) Percentage

in mix Green Blue Grey Total

Canada (non-organic, rainfed) 2069 0 1103 3172 50

France (non-organic, rainfed) 2048 0 603 2651 25

France (non-organic, irrigated) 1255 519 370 2145 25

Soybean mix (for soy burger) 1860 130 795 1860

The water footprints of soy milk and soy burger from the Belgian and Dutch

factories are calculated based on the percentages of soybean intake from different farms.

Figure 2.5 shows the change in the total footprint of 1 litre of soy milk according to farm

location and type of agricultural practice (organic vs. non-organic and rainfed vs. irrigated).

The soybean used as an ingredient in the ‘soy milk product’ is supplied from both Canadian

and Chinese organic farms (50% each). Figure 2.5 shows the total water footprint values of

the same product when soybeans are fully supplied from either the Canadian organic,

Chinese organic, French non-organic rainfed, French non-organic irrigated, or Canadian

non-organic farm. If the soybean were only supplied from the Canadian non-organic farm,

45

the water footprint of 1 litre of soy milk would be 49% larger. If all soybeans were supplied

from the Chinese organic farm, then the water footprint of the soy milk product would be

9% smaller. Shifting from full non-organic (as in the one Canadian farm) to full organic (as

in the other Canadian farm) reduces the grey water footprint related to soybean cultivation

by 98%.

Figure 2.4. The green, blue and grey shares in the total water footprints of 1 litre soy milk and 150 g soy burger.

Figure 2.5. The total water footprint of soy milk with soybean input from different farms

(litres).

0 50 100 150 200 250 300 350 400 450 500

China (organic)

Soymilk

Canada (organic)


France (non-organic)



litres

Green WF

Blue WF

Grey WF

93%

4% 3%

Soymilk

69%4%

27%

Soy burgers

Green WF

Blue WF

Grey WF


Figure 2.6. The total water footprint of soy burger with soybean input from different farms (litres).

The soybean in the 150 g of soy burger is supplied from three different farms: a

non-organic Canadian farm (supplying 50% of the soybean) and two non-organic French

farms, a rainfed one and an irrigated one (both supplying 25%). The total water footprint of

this soy burger is 158 litres (Figure 2.6). If we were to source soybean only from the

Canadian non-organic farm, the total water footprint of our product would be 9% higher.

However, if we sourced soybean from the Chinese organic farm that we studied for the soy

milk case, the total water footprint of our soy burger would decrease by 30%.

2.3.3 Water footprint of soy products versus equivalent animal products

The water footprints of cow’s milk and beef burger have been studied in detail before by

Chapagain and Hoekstra (2004) and recently by Mekonnen and Hoekstra (2010b). In this

study we make use of the estimates from the latter study. In the latter study packing is not

included in the water footprint values. Therefore, for the comparison of cow’s milk and soy

milk, the water footprint of packaging material is added to the water footprint of cow’s milk

(27.8 litres per 1 litre of milk). Similarly, the water footprint of packaging materials is

added to the beef burger for fair comparison with the soy burger (35.5 litres per 150 g of

beef burger). The packing for animal products are taken as same as the soybean products.

Figure 2.7 shows the water footprint of 1 litre of soy milk produced in Belgium in

comparison to the water footprint of 1 litre of cow’s milk from various locations. The

0 50 100 150 200

China (organic)

Canada (organic)



Soy burger


litres

Green WF

Blue WF

Grey WF

47

smallest water footprint of cow’s milk is 540 litres for the UK and the largest is 1800 litres

for Spain, while the world average amounts to 1050 litres.

Figure 2.7. The water footprint of 1 litre of soy milk compared to the water footprint of 1 litre

of cow’s milk from various locations (in litres).

Figure 2.8. The water footprint of 150 g of soy burger compared to the water footprint of 150

g of beef burger from various locations (in litres).

0

500

1000

1500

2000S

oy M

ilk

Uni

ted

Kin

gdo

m

Net

herla

nds

Ge

rma

ny

Fra

nce

Bel

giu

m

Italy

US

A

Can

ada

Glo

bal a

vera

ge

Pak

ista

n

Chi

na

Rus

sia

Gre

ece

Spa

in

litre

s

Grey Blue Green

0500

1000150020002500300035004000

Soy

bur

ger

Net

herla

nds

Uni

ted

Kin

gdom

Ger

man

y

Fran

ce

Bel

gium Ita

ly

Spa

in

Chi

na

Can

ada

US

A

Glo

bal a

vera

ge

Rus

sia

Gre

ece

Pak

ista

n

litre

s

Grey Blue Green


Figure 2.8 compares the water footprint of 150 g of soy burger produced in the

Netherlands with the water footprints of beef burgers from different locations. As seen in

the figure, soy burger has a smaller water footprint (158 litres) than all the beef burgers

from any source. The largest water footprint of beef burger is from Pakistan (3650 litres)

and the lowest is from the Netherlands (1000 litres), while the world average is 2350 litres.

The water footprint values change by location as the climatic, soil conditions and

production systems varies across the countries.

2.4 Discussions

The calculations in this study are based on some assumptions. First, we assumed that the

concentration of the pollutant in the effluent is equal to its actual concentration in the

receiving water body during the production of the products. Therefore, the operational grey

water footprint becomes zero. We introduce this assumption as the wastewater treatment

levels in the Netherlands and Belgium are very high and the regulations for treatment of

industrial wastewater are very strict. Additionally, this assumption has a very little effect

on the total water footprint of the products (less than 1%). Second assumption is that there

is no water loss in the production processes. The water balance recordings of the factories

indicate that water intake is almost equal to the wastewater discharge from the factories.

Therefore, our assumption is very close to the real case. Another assumption is that vanilla

extract used in the products is single folded. It might be the case that vanilla extract is

multi folded; however the effect of this to the total water footprint of soy burger is

negligible.

This study only focuses accounting phase of water footprint assessment and

excludes impact assessments. For a more in-depth analysis of the local environmental and

social impacts of water footprints of products, one would have to analyse the water

footprints in their geographic context, considering for example local water scarcity and

pollution and effects on local ecosystems and social conflict. In the current study, this has

not been done because the interest was not to study local impacts, but to compare the claims

on freshwater resources of soy products versus equivalent animal products and to consider

how the type of agricultural practice (organic versus non-organic; rainfed versus irrigated)

49

can influence freshwater claims as well. Additionally we did not assess and take the

environmental damages due to extensive soybeans production into consideration. Some

possible damages can be deforestation, land degradation and soil pollution (WWF, 2003).

Our analysis also does not reflect the human needs for nutrients such as proteins, calcium. It

may be the case that more soy-product is required to fully match the nutrients that is taken

from animal products.

The case of French farms is a good example of how irrigation can affect the water

footprint value. The two French farms are located in the same region with similar climatic

conditions. However, the first farm irrigates its field to obtain higher yields and the second

farm cultivates soybean only with rainwater. The comparison of the water footprints shows

that soybeans from the irrigated farm have a smaller total water footprint (14%), but the

irrigated soybeans have a five times larger blue water footprint and a larger grey water

footprint as well. This result is important, as generally competition over blue water

resources is larger (i.e. they are scarcer), so that it may well be that from both an economic

and environmental point of view the benefit of the reduced blue and grey water footprints in

rainfed farming exceeds the cost of the increased green water footprint. Obviously, the

analysis presented here is a partial one, focussed on showing green and blue water

consumption and pollution; for a complete assessment of rainfed versus irrigated farming

one needs to take other relevant factors into account as well, like the costs of both practices

and the scarcity of (i.e. the competition over) both the green and blue water resources.

In the example of soy bean cultivation in France, there is space for improving

rainfed soybean yields and therefore reducing the water footprint. This could be done in

number of ways, for example by selecting high-yielding, well-adapted varieties, controlling

weeds prior to planting, planting at the optimum seeding rates, depth and timing, harvesting

at the optimum stage and adjusting combine settings (Staton et al., 2010). The grey water

footprint could also be reduced by shifting to integrated or organic farming systems.

Organic farmers grow crops without using synthetic pesticides or fertilizers,

relying instead on a wide range of cultural practices and alternative inputs believed to be

safer for the environment and the consumer. Soybeans are relatively easy to produce using


organic methods. However, it is important to recognize that organic farms rarely focus on a

single crop. Organic soybean is grown in rotation with several other crops that (ideally)

complement or compensate for one another. Crop rotations serve two primary purposes: to

improve soil fertility and to break pest cycles. With regard to fertility management, rotation

strategies concentrate mainly on generating and conserving nitrogen. Nitrogen is commonly

the most limiting element in organic production, especially for corn and small grains, which

complement soybeans in most crop sequences. Crop rotations that include forage legumes

are the key where nitrogen is supplied to the system (NCAT, 2004). Organic production has

slightly lower water consumption because the evapotranspiration from the field is less

(Allen et al., 1998) and results in much less pollution because the load of chemicals to

groundwater and surface water is less. Organic production systems also have other

environmental benefits beside grey and blue water footprint reductions. Organic

agricultural production systems also have lower ecological footprints (Niccolucci et al.,

2008).

The current study is not based on field measurements of water consumption and

leaching of applied chemicals, but based on statistics supplied by the farms and simple

models to estimate evapotranspiration and water pollution. The figures presented should

therefore be considered as very rough first estimates only.

2.5 Conclusions

This study shows the importance of a detailed supply-chain assessment in water footprint

accounting. Food processing industries commonly consider water use in their own

operations only. If they have water use reduction targets, those targets are formulated with

regard to their own water use. With examples for two soybean products, this study shows

that, however, the operational water footprint is almost negligible compared to the supply-

chain water footprint. For a food processing company, it is crucial to recognize farmers as

key players if the aim is to reduce the overall water consumption and pollution behind final

food products. Engaging with farmers and providing positive incentives for the adoption of

better agricultural practices are an essential element in a food company’s effort to make its

products sustainable.

51

The results of the study show that the water footprint of a soy product is very

sensitive to where the inputs of production are sourced from and under which conditions

the inputs are produced. This is most in particular relevant for the agricultural inputs. The

water footprints of soy milk and soy burger depend significantly on the locations of the

farms producing the soybean and on the agricultural practices at these farms (organic vs.

non-organic and rainfed vs. irrigated). Not only the total water footprint, but also the colour

composition (the ratios green, blue, and grey) strongly varies as a function of production

location and agricultural practice. These results reveal the importance of the spatial

dimension of water accounting.

For the limited number of cases that we have considered, we find that non-organic

soybean has a larger water footprint (ranging between 2145-3172 m3/ton) than organic

soybean (1520-2024 m3/ton). Organic agriculture, apart from having a lower

evapotranspiration, reduces the grey water component. Shifting towards organic production

will reduce the grey water footprint of agricultural production and thus the damage to

aquatic life and ecosystems. Another factor that can be influenced is the degree of

irrigation. In the case of the two French farms considered in this study, the total water

footprint is larger for rainfed soybean, but the blue water footprint of rainfed soybean is

zero.

The study shows that soy milk and soy burger have much smaller water footprints

than their equivalent animal products. The water footprint of the soy milk product analysed

in this study is 28% of the water footprint of the global average cow milk. The water

footprint of the soy burger examined here is 7% of the water footprint of the average beef

burger in the world.

3 Corporate water footprint accounting and impact assessment: The case of the water footprint of a sugar-containing carbonated beverage2

Abstract

All water use in the world is ultimately linked to final consumption by consumers. It is

therefore interesting to know the specific water requirements of various consumer goods,

particularly the water-intensive ones. This information is relevant not only for consumers,

but also for food processors, retailers, and traders. The objective of this paper is to carry out

a pilot study on water footprint accounting and impact assessment for a hypothetical sugar-

containing carbonated beverage in a 0.5 litre PET-bottle produced in a hypothetical factory

that takes its sugar alternatively from sugar beet, sugar cane and high fructose maize syrup

and from different countries. The composition of the beverage and the characteristics of the

factory are hypothetical but realistic. The data assumed have been inspired by a real case.

This paper does not only look at the water footprint of the ingredients of the beverage, but

also at the water footprint of the bottle, other packaging materials and construction

materials, paper and energy used in the factory. Although most companies focus on their

own operational performance, this paper shows that it is important to consider freshwater

usage along the supply chain. The water footprint of the beverage studied has a water

footprint of 150 to 300 litres of water per 0.5 litre bottle, of which 99.7-99.8% refers to the

supply chain. The study also shows that agricultural ingredients that constitute only a small

fraction in weight of the final product have the biggest share at the total water footprint of a

product.

3.1 Introduction

Freshwater in sufficient quantities and adequate quality is a prerequisite for human societies

and natural ecosystems (Costanza and Daly, 2002). Today, around 70% of the total

freshwater withdrawal by humans is for irrigated agricultural use (Gleick, 1993; Bruinsma,

2 Based on Ercin et al. (2011)

54/ Chapter 3. The water footprint of a sugar-containing carbonated beverage

2003; Shiklomanov and Rodda, 2003; UNESCO, 2006). Agriculture as a whole is

responsible for about 86% of the worldwide freshwater use (Hoekstra and Chapagain,

2007). Agriculture has to compete with other water users like municipalities and industries

(Rosegrant and Ringler, 1998; UNESCO, 2006). Freshwater is a basic ingredient for many

companies’ operations, and effluents may pollute the local hydrological ecosystems. Many

companies have addressed these issues and formulated proactive management strategies

(Gerbens-Leenes et al., 2003). A company may face four serious risks related to failure to

manage the freshwater issue: damage to the corporate image, the threat of increased

regulatory control, financial risks caused by pollution and insufficient freshwater

availability for business operations (Rondinelli and Berry, 2000; WWF, 2007).

The water footprint is an indicator of water use that looks at both direct and

indirect water use of a consumer or producer. The water footprint of an individual,

community or business is defined as the total volume of freshwater that is used to produce

the goods and services consumed by the individual or community or produced by the

business (Hoekstra and Chapagain, 2008). Water use is measured in terms of water volumes

consumed (evaporated or incorporated into the product) and polluted per unit of time. The

water footprint is a geographically explicit indicator, not only showing volumes of water

use and pollution, but also the locations. Compared to other water accounting tools, water

footprint provides the most extended and complete water accounting method, since it

includes both direct and indirect water use and considers both water consumption and

pollution. It has already been applied for various purposes, such as the calculation of the

water footprint of a large number of products from all over the world (Chapagain and

Hoekstra, 2004), but so far there have been few applications for business accounting.

The objective of this paper is to carry out a pilot study on water footprint

accounting and impact assessment for a hypothetical sugar-containing carbonated beverage

in a 0.5 litre PET-bottle produced in a hypothetical factory that takes its sugar alternatively

from sugar beet, sugar cane and HFMS (high fructose maize syrup) sourced from different

countries. The aim is primarily to learn from the practical use of existing water footprint

accounting and impact assessment methods and to refine these methods and develop

practical guidelines. The whole assessment has been inspired by a real case. From a

55

scientific point of view, this paper aims to assess the necessary scope of analysis and, in

particular, to explore the degree of detail required in such a study. Finally, an impact

assessment of the water footprints is carried out, identifying the hotspots or high-risk areas.

3.2 Method

This study estimates the water footprint of a hypothetical 0.5 litre PET-bottle sugar-

containing carbonated beverage. It looks into more detail at the water footprint of the sugar

input, by considering three different sources (sugar beet, sugar cane and HFMS) and

various countries of origin. The water footprint of different ingredients and other inputs is

calculated distinguishing the green, blue and grey water components. The green water

footprint refers to the global green water resources (rainwater) consumed to produce the

goods and services. The blue water footprint refers to the global blue water resources

(surface water and ground water) consumed to produce the goods and services.

‘Consumption’ refers here to ‘evaporation’ or ‘incorporation into the product’. It does not

include water that is withdrawn but returns to the system from where it was withdrawn. The

grey water footprint is the volume of polluted water that associates with the production of

goods and services. The calculation methods applied in this study follow Hoekstra et al.

(2009).

The total water footprint of a business contains various components as shown in

Figure 3.1. The ‘business’ considered in this paper refers to the part of the factory that

produces our 0.5 litre PET bottle sugar-containing carbonated beverage. The factory

produces also other products, but this falls outside the scope of this paper. The water

footprint of our product includes both an operational water footprint and a supply-chain

water footprint. The operational (or direct) water footprint is the volume of freshwater

consumed or polluted in the operations of the business itself. The supply-chain (or indirect)

water footprint is the volume of freshwater consumed or polluted to produce all the goods

and services that form the input of production of the business. Both operational and supply-

chain water footprint consist of two parts: the water footprint that can be directly related to

inputs applied in or for the production of our product and an overhead water footprint. In

both cases, we distinguish between a green, blue and grey water footprint.


Figure 3.1. Composition of the water footprint of a business.

Figure 3.2 shows the production system of our product. It shows the four main

ingredients of the beverage (water, sugar, CO2 and syrup for flavouring) and the other main

inputs of production (bottle, cap, label and glue, packing materials).

The production system shown in Figure 3.2 does not show the overhead of

production. The overhead of production refers to all inputs used that cannot be solely

attributed to the production of the specific product considered. The overhead water

footprint refers to freshwater use that in first instance cannot be fully associated with the

production of the specific product considered, but refers to freshwater use that associates

with supporting activities and materials used in the business, which produces not just this

specific product but other products as well. The overhead water footprint of a business has

to be distributed over the various business products, which is done based on the relative

value per product. The overhead water footprint includes, for example, the freshwater use

Blue water footprint

Green water footprint

Wat

er

pollu

tion

Wat

er

cons

umpt

ion



Grey water footprintGrey water footprint

Water footprint of a business

Operational water footprint

Operational water footprint directly associated with the production of the product

Supply-chain water footprint

Overhead operational water footprint

Supply-chain water footprint related to the product inputs

Overhead supply-chain water footprint



Wat

er

pollu

tion

Wat

er

cons

umpt

ion






Wat

er

pollu

tion

Wat

er

cons

umpt

ion



Grey water footprintGrey water footprintGrey water footprintGrey water footprint

Water footprint of a business









Wat

er

pollu

tion

Wat

er

cons

umpt

ion






Wat

er

pollu

tion

Wat

er

cons

umpt

ion



Grey water footprintGrey water footprintGrey water footprintGrey water footprint

57

in the toilets and kitchen of a factory and the freshwater use behind the concrete and steel

used in the factory and machineries.

Figure 3.2. Production system of the 0.5 litre PET-bottle sugar-containing carbonated

beverage.

3.3 Data sources and assumptions

For the assessment, we have formulated a hypothetical sugar-containing carbonated

beverage in a 0.5 litre PET-bottle and a hypothetical factory that takes its sugar

alternatively from sugar beet, sugar cane and HFMS (high fructose maize syrup) sourced

from different countries. The factory itself is assumed to be in the Netherlands, but many of

the inputs come from other countries. The composition of the beverage and the

characteristics of the factory are hypothetical but realistic. The set of data assumed has been

inspired by a real case.

SugarWater Treatment

Syrup Preparation

Proportioner

CO2

Carbonator

Filler

H2O Syrup

PET Resin

Bottle Making

CAP (PE)

Closing

Film (PP)Glue

Labelling

Packing

CartoonPaper

Overhead

Final Product


3.3.1 Operational water footprint


The following components are defined as operational water footprint:

- Water incorporated into the product as an ingredient.

- Water consumed (i.e. not returned to the water system from where it was withdrawn)

during the production process (during bottling process, washing, cleaning, filling,

labelling and packing).

- Water polluted as a result of the production process.

The first two components form the blue operational water footprint; the third

component forms the operational grey water footprint. There is no use of green water

(rainwater) in the operations, so there is no operational green water footprint.

The water used as ingredient is 0.5 litres per bottle. The production of our

beverage includes the following process steps: bottle making (from PET resins to PET-

bottle forms), bottle cleaning (by air), syrup preparation, mixing, filling, labelling and

packing. During all these processes, there is no water consumption.

All wastewater produced during the production steps of the beverage is treated at a

municipal wastewater treatment plant. The concentrations of chemicals in the effluent of

the wastewater treatment plant are equal and in some instances even lower than the natural

concentrations in the receiving water body. With this assumption, the grey component of

the operational water footprint is effectively zero.


The overhead operational water footprint is the water consumed or polluted as a result of:

- Water consumption by employees (drinking water).

- Water consumption or pollution as a result of water use in toilets and kitchen.

- Water consumed or polluted due to washing of the working clothes of the employees.

- Water consumed or polluted due to cleaning activities in the factory.

59

- Water consumption in gardening.

The factory considered in this study produces a number of different beverage

products; our beverage is just one of them. Therefore, only a fraction of the total overhead

water footprint is attributed to our beverage product, based on the ratio of the annual value

related to the production of this specific product to the annual value of all products

produced in the factory. The annual production value of our beverage product is 10% of the

total production value of all beverage products produced by the factory.

In this study we assume that drinking water is negligible and that there is no

gardening. It is further assumed that all water used during the other activities specified

above returns to the public sewerage system and is treated in a municipal wastewater

treatment plant such that the effluent causes no grey water footprint. As a result, the

overhead operational water footprint is estimated as zero.

3.3.2 Supply-chain water footprint


The supply-chain water footprint related to product inputs consists of the following components:

- Water footprint of product ingredients other than water (sugar, CO2, phosphoric acid,

caffeine from coffee beans, vanilla extract, lemon oil and orange oil).

- Water footprint of other inputs used in production (bottle, cap, labelling materials,

packing materials).

Table 3.1 specifies, per ingredient, the precise amount contained in a 0.5 litre

bottle. It also shows which raw material each ingredient underlies and what the country of

origin of the raw material is. In the case of sugar, the study considers three alternative

sources: sugar beet, sugar cane and maize (which are used to make high fructose maize

syrup). Table 3.1 also specifies the amounts of the other inputs used, again per 0.5 litre

bottle. The figures for the amounts used are based on realistic values, similar to the ones on

the commercial market. During bottle production, 25% of the material consists of recycled


material. This ratio is taken into account in the calculations by using a fraction of 0.75 to

calculate the amount of new material used. A similar approach has been used for pallets,

which have a lifespan of 10 years (fraction 0.1 applied to the total used).

Table 3.1. Ingredients and other items used for the sugar-containing carbonated beverage (per 0.5 litre bottle).

Item Amount (grams) Raw material Origin of raw material

Sugar 501 Sugar beet Iran, Russia, USA, Italy, Spain, France, The Netherlands

Sugar 501 Sugar cane Cuba, Pakistan, Brazil, India, Peru, USA

Sugar 501 HFSM India, USA, France, China

CO2 4 Ammonia by product The Netherlands

Caffeine 0.05 Coffee beans Colombia

Phosphoric acid 0.2 Phosphate rock – by chemical process USA

Vanilla extract 0.01 Vanilla beans Madagascar

Lemon oil 0.007 Lemon World market

Orange oil 0.004 Orange World market

Bottle - PET 19.5 Oil World market

Closure - HDPE 3 Oil World market

Label - PP 0.3 Oil World market

Label glue 0.18 Glue World market

Tray glue 0.015 Glue World market

Tray cartoon - paperboard 2.8 Wood World market

Tray shrink film - PE 1.6 Oil World market

Pallet stretch wrap - PE 0.24 Oil World market

Pallet label (2x) - coated paper 0.003 Wood World market

Pallet - painted wood 0.09 Wood World market

1 Breedveld et al. (1998).

61

Table 3.2. Water footprint of the ingredients of the sugar-containing carbonated beverage.

1 Van der Leeden et al. (1990). 2 Dignum et al. (2001)

For the beverage ingredients, data on the water footprints of the raw materials,

process water requirements, and product and value fractions, are presented in Table 3.2.

The water footprints of the various forms of sugar from different countries have been taken

Item Raw material

Selected location

Water footprint of raw material (m3/ton)

Process water requirement (m3/ton)

Fractions for products used


Value fraction

Sugar Sugar beet Iran 21 298 36 0 0 0 0.16 0.89

Sugar Sugar beet Russia 89 123 16 0 0 0 0.16 0.89

Sugar Sugar beet USA 53 108 23 0 0 0 0.16 0.89

Sugar Sugar beet Italy 50 56 19 0 0 0 0.12 0.89

Sugar Sugar beet Spain 29 67 28 0 0 0 0.13 0.89

Sugar Sugar beet France 36 29 19 0 0 0 0.14 0.90

Sugar Sugar beet Netherlands 45 23 18 0 0 0 0.15 0.89

Sugar Sugar cane Cuba 310 214 20 0 0 0 0.14 0.86

Sugar Sugar cane Pakistan 29 402 26 0 0 0 0.14 0.86

Sugar Sugar cane Brazil 115 87 8 0 0 0 0.14 0.86

Sugar Sugar cane India 85 156 15 0 0 0 0.14 0.86

Sugar Sugar cane Peru 0 134 8 0 0 0 0.14 0.86

Sugar Sugar cane USA 95 79 10 0 0 0 0.14 0.86

Sugar HFMS India 1163 376 100 0 0 0 0.36 0.73

Sugar HFMS USA 156 136 64 0 0 0 0.36 0.73

Sugar HFMS France 100 99 90 0 0 0 0.36 0.73

Sugar HFMS China 328 177 118 0 0 0 0.36 0.73

CO2 Ammonia by product USA 0 0 0 0 83.51 0 1 1

Phosphoric acid

Phosphate rock USA 0 0 0 0 0 0 1 1

Caffeine Coffee beans Colombia 14470 0 0 0 0 0 0.0137 1

Vanilla extracVanilla Madagascar 199383 0 0 0 0 0 0.0252 1

Lemon oil Lemon World average 559 0 0 0 0 0 0.4 1

Orange oil Orange World average 457 0 0 0 0 0 0.0021 1


mainly from Gerbens-Leenes and Hoekstra (2009). For four selected countries (France,

Italy, Spain and the Netherlands), the water footprint of sugar beet is specifically calculated

as part of the scope of this study. The water footprints of other ingredients are taken from

Chapagain and Hoekstra (2004). For the other inputs used in the production of a 0.5 litre

bottle of our beverage, water footprints of raw materials and process water requirements are

presented in Table 3.3.

Table 3.3. Water footprint of raw materials and process water requirements for other inputs

of a 0.5 litre bottle of sugar-containing carbonated beverage.

Item Raw material

Selected location

Water footprint of raw material (m3/ton)1

Process water requirement (m3/ton)1

Green Blue Grey Green Blue Grey

Bottle -PET Oil Sweden (raw) - Germany (process) 0 10 0 0 0 225

Closure - HDPE Oil Sweden (raw) -

Germany (process) 0 10 0 0 0 225

Label - PP Oil Sweden (raw) - Germany (process) 0 10 0 0 0 225

Label glue Glue Germany 0 0 0 0 0 0 Tray glue Glue Netherlands 0 0 0 0 0 Tray cartoon Wood Belgium 369.42 0 0 0 0 180

Tray shrink film Oil Sweden (raw) - Germany (process) 0 10 0 0 0 225

Pallet stretch wrap Oil Sweden (raw) -

Germany (process) 0 10 0 0 0 225

Pallet label Wood Finland (process) 369.42 0 0 0 125

Pallet Wood Sweden (process) - Russia 369.42 0 0 0 75

1 Van der Leeden et al. (1990). 2 Gerbens-Leenes et al. (2009a).


The overhead supply-chain water footprint originates from all goods and services used in

the factory that are not directly used in or for the production process of one particular

63

product produced in the factory. The factory produces other products than our 0.5 litre PET

bottle of sugar-containing carbonated beverage as well, so the overhead water footprint

needs to be allocated only partly to our product.

Goods that could be considered for the calculation of the overhead supply-chain

water footprint are for example: construction materials and machineries used in the factory,

office equipment and materials, cleaning equipment and materials, kitchen equipment and

materials, working clothes used by employees, transportation, and energy for heating and

power. This list can be extended further. For the scope of this study, it was decided to

include some selected materials for the calculation of overhead water footprint in order to

understand the influence of such elements on the total water footprint of the final product.

The materials selected for assessment are the following:

- Construction materials (concrete and steel)

- Paper

- Energy in the factory (natural gas and electricity)

- Transportation (vehicles and fuel)

Table 3.4. List of selected goods and services for assessing the overhead supply-chain

water footprint.

The amounts of materials used in our factory are specified in Table 3.4. For paper

and energy use in the factory and transportation fuels, annual amounts are given. For

Item Total amount used

Unit Raw material

Amount of raw material

Unit of raw material

Lifespan of material

Yearly amount

Concrete 30000 ton Cement 30000 ton 40 750 Steel 5000 ton Steel 5000 ton 20 250 Paper 1 ton/year Wood 1 ton/year - 1 Natural gas 65000 GJ/year Gas 65000 GJ/year - 65000

Electricity 85000 GJ/year Several 85000 GJ/year - 85000 Vehicles 40 numbers Steel 11.6 tons/vehicle 10 46.4 Fuel 150000 litres/year Diesel 150000 litres/year - 150000


construction materials and vehicles, total amounts are given with a specification of the

lifespan of the totals. The lifespan can be used to calculate annual figures from the totals.

For the vehicles, it is assumed that average lifespan of a track is 10 years. Table 3.5 gives

the water footprints of the raw materials relating to the overhead goods and the process

water requirements.

The value of the 0.5 litre PET bottles of our beverage is 10% of the total value of

products produced in the factory. Therefore, 10% of the total overhead water footprint of

the factory will be allocated to our product. The annual production is 30 million bottles per

year, so the overhead water footprint per bottle is found by dividing the overhead water

footprint insofar allocated to our product by 30 million.

Table 3.5. Supply-chain water footprint of the selected overhead goods and services.

1 Van der Leeden et al. (1990).

2 Gerbens-Leenes et al. (2009a).

Item Raw material

Selected location for the calculation of the water footprint

Water footprint of raw material (m3/ton)

Process water requirement (m3/ton)1

Green2 Blue1 Grey Green Blue Grey

Concrete Cement Belgium (process) 0 0 0 0 0 1.9

Steel Steel Sweden (process) - USA (raw material) 0 4.2 0 0 0 61

Paper Wood Finland (process) 369.4 0 0 0 0 125 Natural gas Gas World average 0 0 0 0 0 0.11

Electricity Several World average 0 0 0 0 0 0.47

Vehicles Steel Sweden (process) - USA (raw material) 0 4.2 0 0 0 61

Fuel Diesel World average 0 0 0 0 0 1.06

65

3.4 Results

3.4.1 Water footprint of a 0.5 litre PET-bottle sugar-containing carbonated beverage

The total water footprint of our beverage amounts to 169 to 309 litres (Table 3.6). In

calculating the total water footprint of the product, the amounts of all ingredients and other

inputs are kept constant; only the type and origin of the sugar is changed in order to

understand the effect of sugar type and production location on the total water footprint of

the beverage. The effect of the type and origin of sugar used is shown in Figure 3.3.

Table 3.6. The total water footprint of a 0.5 litre PET-bottle sugar-containing carbonated

beverage.

Item


Green Blue Grey Total

Operational water footprint 0 0.5 0 0.5

Supply-chain water footprint* 134.5-252.4 7.4-124 9.2-19.7 168-308.9

Total* 134.5-252.4 7.9-124.5 9.2-19.7 168.5-309.4

*The range reflects the fact that we have considered different types and origin of the sugar input.

The total water footprint of the beverage is the highest (309 litres) when the sugar

originates from cane sugar from Cuba, and the lowest (169 litres) when the sugar comes

from beet sugar from the Netherlands. If we compare the beet sugars, our product has the

highest water footprint when beet sugar is from Iran (241 litres) followed by Russia (206

litres), USA (194 litres), Italy (189 litres), Spain (185 litres), France (170 litres) and the

Netherlands (169 litres). For sugar cane, our beverage has the highest water footprint when

we take the cane from Cuba (309 litres), followed by Pakistan (283 litres), India (221

litres), Brazil (207 litres), USA (199 litres) and Peru (186 litres). When we use HFMS as a

sweetener, the order is: India (309 litres), China (206 litres), USA (179 litres) and France

(172 litres).


Almost the entire water footprint of the product is stemming from the supply-chain

water footprint (99.7-99.8%). This shows the importance of a detailed supply chain

assessment. Common practice in business water accounting, however, is to focus on

operational water consumption. The results of this study imply that compared to the

traditional water use indicator (water withdrawal for the own operations), the water

footprint provides much more information. In this particular case, the operational water

footprint cannot be lowered because it is precisely equal to the amount needed as an

ingredient to the beverage. The traditional indicator of water withdrawal would show a

larger number, because withdrawals include return flows, while the water footprint

excludes those, because return flows can be reused, so they do not impact on the available

water resources like consumptive water use does. In our case, there is no consumptive

water use and wastewater is treated properly before returned to the system.

Figure 3.3 The total water footprint of 0.5 litre PET-bottle sugar-containing carbonated

beverage according to the type and origin of the sugar (SB=Sugar Beet, SC=Sugar Cane,

HFMS= High Fructose Maize Syrup).

100

150

200

250

300

350

Net

herla

nds

SB

Fran

ce S

B

Fran

ce H

FMS

US

A H

FMS

Spa

in S

B

Per

u S

C

Italy

SB

USA

SB

USA

SC

Chi

na H

FMS

Braz

il S

C

Rus

sia

SB

Indi

a S

C

Iran

SB

Paki

stan

SC

Indi

a H

FMS

Cub

a S

C

WF

(litr

es)

Grey WFBlue WFGreen WF

67

Figure 3.4 shows the colour composition of the total water footprint of the product

for two different countries. Pakistan has the one with the highest ratio for blue water

footprint. The Netherlands has the highest ratio for green water footprint.

Figure 3.4 The water footprint colour composition of a 0.5 litre PET-bottle sugar-containing

carbonated beverage for Pakistan (sugar cane) and the Netherlands (sugar beet).


The supply-chain water footprint of our beverage is calculated as a summation of the water

footprints of all inputs (both ingredients and other inputs) and the water footprint of

overhead activities. Table 3.7 presents the various components of the supply-chain water

footprint of our beverage product.

Sugar is one of the main water consuming ingredients in our beverage. One of the

aims of this paper is to understand the effect of sugar type and origin on the total water

footprint of the beverage. For this purpose, three different commonly used sugar types are

selected: sugar beet, sugar cane and HFMS. For each type, some production countries are

selected for the calculation, which have high, low and average water footprints. Table 3.8

presents the water footprint of the sugar input in our beverage product as a function of

sugar type and origin.

Sugar Cane- Pakistan

44%

5%

51%

Sugar Cane- Pakistan

44%

5%

51%

Sugar Beet- the Netherlands

88%

5%7%

Green WF

Blue WF

Grey WF

Sugar Beet- the Netherlands

88%

5%7%

Green WF

Blue WF

Grey WF


Table 3.7. The supply-chain water footprint of a 0.5 litre PET-bottle sugar-containing

carbonated beverage.

Item Supply-chain water footprint (litres)

Green Blue Grey Total

Sugar see Table 3.8

CO2 0 0.3 0 0.33

Phosphoric acid or citric acid (e338) 0 0 0 0

Caffeine 52.8 0 0 52.8

Vanilla extract 79.8 0 0 79.8

Lemon oil 0.01 0 0 0.01

Orange oil 0.9 0 0 0.9

Bottle – PET 0 0.2 4.4 4.5825

Closure – HDPE 0 0.03 0.68 0.7

Label – PP 0 0.003 0.068 0.07

Label glue (not included) 0 0 0 0

Tray glue (not included) 0 0 0 0

Tray cartoon - paperboard 1 0 0.5 1.5

Tray shrink film - PE 0 0.02 0.36 0.38

Pallet stretch wrap - PE 0 0.003 0.054 0.057

Pallet label (2x) - coated paper 0.001 0 0.0004 0.0015

Pallet - painted wood 0.033 0 0.007 0.04

Concrete 0 0 0.005 0.005

Steel 0 0.004 0.05 0.054

Paper 0.0012 0 0.0004 0.0016

Natural Gas 0 0 0.024 0.024

Electricity 0 0 0.13 0.13

Vehicles 0 0.001 0.009 0.01

Fuel 0 0 0.5 0.5

Total 134.5-252.4 7.4-124 9.2-19.7 168-308.9

When we choose to use sugar beet as sugar source of our hypothetical beverage,

the water footprint of the sugar input can vary from 26 litres per 0.5 litre bottle (when the

sugar beets are grown in the Netherlands) to 98.5 litres (Iran). If our source is sugar cane,

the water footprint of the sugar input can vary from 43.9 litres per bottle (Peru) to 167 litres

(Cuba). If we would use HFMS as a sweetener, not so usual in the world but common in the

69

US, the water footprint of the sugar input will range from 29.3 litres per bottle (when the

maize comes from France) to 166 litres (India). It is important to identify and analyse the

colours of the water footprint of the product in order to assess the impacts of the water

footprints. The highest blue water footprint related to the sugar input alone is 124 litres with

sugar cane from Pakistan and the lowest is 7 litres with sugar beet from the Netherlands.

The grey water footprint of the sugar input is the lowest when the sugar intake is cane sugar

from Brazil (2.4 litres), and highest with HFMS from China (12 litres). This analysis shows

that sugar type and production location affect the total water footprint of the product and

the ratios green/blue/grey significantly. It shows that including the spatial dimension in

water footprint assessment is indeed important.

Table 3.8. The water footprint of the sugar input for a 0.5 litre PET-bottle sugar-containing


Item Water footprint (litres)

Remarks1 Green Blue Grey Total

Beet sugar Iran1 5.7 82.8 10.0 98.5 Highest WF, highest blue WF Russia1 24.6 34.1 4.5 63.3 High WF, big producer USA1 14.7 30.1 6.4 51.2 Second biggest producer in the world Italy2 18.6 20.8 7.1 46.5 Close to global average WF Spain2 10.0 23.1 9.7 42.8 Close to global average WF France2 11.7 9.5 6.2 27.4 Biggest producer in the world Netherlands2 13.6 7.0 5.4 26.0 Very low WF Cane sugar1 Cuba 95.2 65.7 6.2 167.0 Highest WF Pakistan 9.0 123.5 8.0 140.4 High WF, highest blue WF Brazil 35.3 26.6 2.4 64.3 Biggest producer in the world India 26.2 47.9 4.6 78.6 Second biggest producer in the world Peru 0.0 41.3 2.6 43.9 Lowest WF USA 29.3 24.4 3.2 56.8 Close to world average HFMS 551 India 117.9 38.2 10.2 166.2 Highest WF

USA 15.9 13.8 6.5 36.1 Biggest producer in the world and highest rate of maize usage for sugar input

France 10.1 10.0 9.2 29.3 Low WF China 33.3 17.9 12.0 63.2 Close to global average WF

1 Gerbens-Leenes and Hoekstra (2009). 2 Own calculations.


In our hypothetical beverage, the amounts of vanilla extract (0.01 g) and caffeine

from coffee beans (0.05 g) inputs are very small in the total amount of the beverage.

Although their physical content in the beverage is small (0.09% for caffeine and 0.02% for

vanilla), their contribution to the total water footprint of the product is very high (maximum

33% for caffeine and 50% for vanilla). This study reveals that, without prior knowledge

about the relevance of different inputs, a detailed and comprehensive supply-chain analysis

is essential for the calculation of the water footprint of a product. Even small ingredients

can significantly affect the total water footprint of a product.


The operational water footprint of a 0.5 litre PET-bottle sugar-containing carbonated

beverage has a number of components as shown in Table 3.9. Both green and grey water

footprints are zero. The blue water footprint is 0.5 litre of water for one bottle. The total

operational water footprint is thus no more than the water used as ingredient of the

beverage. The ‘water footprint’ of the operations is lower than the ‘water withdrawal’ of the

factory, because all water withdrawn by our hypothetical factory is returned (except for the

water used as ingredient for the beverage) and purified before disposal.

Table 3.9. The operational water footprint of a 0.5 litre PET-bottle sugar-containing


Item

Operational water footprint (litres)

Green Blue Grey Total Inputs Direct water used for a 0.5 litre PET (as ingredient) 0 0.5 0 0.5 Net water used in production steps 0 0 0 0 Bottle making 0 0 0 0 Bottle cleaning (by air) 0 0 0 0 Ingredients mixing 0 0 0 0 Packing 0 0 0 0

Overhead Domestic Water Consumption 0 0 0 0

Total operational water footprint 0 0.5 0 0.5

71

3.5 Impact assessment of a 0.5 litre PET-bottle sugar-containing carbonated beverage

According to its definition, the water footprint concept is a geographically explicit

indicator, not only showing volumes of water use and pollution, but also showing the

various locations where the water is used (Hoekstra and Chapagain, 2008). This means,

water footprint analysis of a business/product shows the impact of business activities on

nature and society by answering two fundamental questions: where (location) and when

(time). It is also useful to show the blue, green and grey components of the water footprint

of a business/product, because the impact of the water footprint will depend on whether it

concerns the evaporation of abstracted ground or surface water, the evaporation of

rainwater used for production or pollution of freshwater.

Assessment of the impacts of a water footprint starts with quantifying, localizing

and describing the colour of the water footprint. Next step is to identify the vulnerability of

the local water systems where the footprint is located, the actual competition over the water

in these local systems and the negative externalities associated with the use of the water.

This kind of an assessment may lead to a corporate water strategy to reduce and offset the

impacts of the water footprint (Hoekstra, 2008). The goals of a business with respect to

reducing and offsetting the impacts of its water footprint can be prompted by the goal to

reduce the business risks related to its freshwater appropriation. Alternatively, they can

result from governmental regulations with respect to water use and pollution.

It is important to understand and evaluate the environmental impacts of all crops if

we are to achieve sustainable production systems. Understanding the impact of sugar beet,

sugar cane and HFMS are particularly important as there are different countries where they

can be grown, and also because there is a growing interest in their potential as a source for

biofuel (Gerbens-Leenes and Hoekstra, 2009).

For the impact assessment of sugar usage, we compare the water footprint of sugar

beet, cane and HFMS as quantified in the previous section with the water scarcity in the

different regions where the water footprint is located following the method developed by

Van Oel et al. (2008). For this purpose, a water scarcity indicator by Smakhtin et al.


(2004a; 2004b) was used. This indicator deals with the withdrawal-to availability ratio per

river basin taking into account the environmental water requirements, which are subtracted

from runoff.

Sugar beet

With a population of more than 65 million people, Iran is actually one of the most water-

scarce countries of the world. It is estimated that the average annual supply of renewable

freshwater per person will fall from 1,750 (2005) to 1,300 m3 (2020). According to the

‘Falkenmark thresholds’, a country will experience periodic water stress when freshwater

availability is below 1,700 m3 per person per year (Falkenmark and Rockström, 2004).

More than 94 percent of the total annual water consumption in Iran is used for agriculture,

so agriculture plays a significant role in water stress in the country. In addition, the

productivity of water (yield per unit of water) is very low. The water footprint of Iranian

sugar beet is one of the highest in the world (Gerbens-Leenes and Hoekstra, 2009). The

Iranian sugar beet usage in our product leads to 99 litres of water consumption per bottle,

84% of which are from blue water sources. Amongst all countries, sugar beet cultivation in

Iran requires the most irrigation (highest blue water footprint). This leads to serious water

problems in sugar beet cultivation regions, especially where the production rate is high.

One-third of the country's sugar factories are in the three provinces of Razavi Khorasan,

Northern Khorasan and Southern Khorasan Iran that experience mostly arid climatic

conditions and currently experiencing extreme water shortages. This problem has become

more visible, especially in these specific parts of the country, due to recent droughts

(Larijani, 2005).

Another country with a high water footprint of sugar intake is Russia with a sugar-

related water footprint of 63 litres per bottle. Similar to Iran, the blue water footprint of

sugar beet in Russia is high, i.e. 53% of the total water footprint. The most important

problem due to sugar beet cultivation in Russia is in the area north of the Black Sea.

Pollution in the rivers Dnieper and Don, which flow to the Black Sea, is causing serious

environmental damage to the Black Sea ecosystem. In 1992, The Russian Federation’s

Committee on Fishing reported several cases of water bodies were completely

73

contaminated by agricultural runoff. Besides pollution by excessive use of fertilizers,

irrigation has also resulted in water scarcity in some areas (Gerbens-Leenes and Hoekstra,

2009).

Andalucia is a clear hotspot since it is a water scarce region with a high water

footprint in relation to sugar beet production. Sugar beet irrigation in this region has

contributed to lower water levels in the Guadalquivir River, limiting water reaching

important wetlands during summer (WWF, 2004).

The water quality issue is a major concern since the overuse of fertilizers on beet

crops is typical of farming in general (WWF, 2004). Environmental impacts generally arise

because the nutrients in the fertilizers are not entirely taken up by the crop but move into

the environment. The runoff of nitrate and phosphate into lakes and streams can contribute

to accelerated eutrophication and the proliferation of toxic microalgae. In the Seine-

Normandy basin, irrigation has little quantitative impact on the resource, but does,

however, have an indirect impact on quality because it favours intensive farming techniques

and spring crops, which leave the soil bare for long periods of the year and increase the

chemical load in the rivers by leaching and draining (UNESCO, 2003). This has a harmful

effect on both the environment and other water uses. Improving water quality is still a

major concern of the basin, where non-point source pollution from farming and urban areas

is still a major problem as nitrate, pesticide and heavy metal concentrations continue to

increase (ibid.).

Sugar cane

Sugarcane is the most important plant in Cuba and it was the most important foreign

exchange earner of the tropical island for decades. The water footprint of sugar intake for

our beverage is the highest when sugar is sugar cane sourced from Cuba, with 167 litres per

bottle. Sugar cane production in Cuba has also the highest water footprint in the world

compared to other sugar types and production locations. Cuba has been facing several

environmental problems for the last decades in relation to sugar cane production. Cuba has

high-quality resources of karst water, but the quality of this water is highly susceptible to


pollution. Pollution resulting from sugar cane factories is one of the main reasons that the

quality of karst aquifers has deteriorated (León and Parise, 2008). In addition, the untreated

wastewater discharge from sugar factories in Cuba has led to oxygen deficiency in rivers

and the dominance of aquatic macrophytes, which results in thick mats of weeds. This

situation partially blocks the water delivery capacity of canals, which has negative effects

on fishing and tourism (WWF, 2004). Due to sugar cane cultivation, deforestation in Cuba

has become a major environmental problem (Monzote, 2008). Cuba’s forest area has also

been drastically decreased as a result of demand for lumber; the sugar cane industry alone

annually consumes 1 million cubic meters of firewood (Cepero, 2000).

Another country with a high water footprint of sugar cane is Pakistan. If we

choose Pakistani sugar cane for our product, the water footprint of sugar intake will be 140

litres per bottle. The sugar cane in Pakistan heavily depends on irrigation; the blue water

footprint constitutes 88% of the total water footprint. Water abstractions for irrigation cause

water shortage in the production regions and serious environmental problems. The Indus

River is the major water resource of Pakistan. The freshwater reaching the Indus Delta has

significantly decreased (90%) as a result of over-usage of water sources in the Indus basin.

Sugar cane is one of the main water consuming agricultural products in the basin. The

decrease in freshwater flow to the Indus Delta has negative impacts on the biodiversity of

the Delta (decrease of mangrove forestlands, and danger of extinction of the blind river

dolphin). Additionally, excessive water use in sugar cane cultivation areas also leads to

salinity problems in Pakistan (WWF, 2004). Moreover, untreated wastewater discharge

from sugar mills causes depletion of available oxygen in water sources which results in

endangering fish and other aquatic life (Akbar and Khwaja, 2006).

Being the largest sugar cane producer in the world, Brazil has faced several

negative impacts of sugar cane production. However, most of the sugar cane produced is

used as raw material for ethanol production. Extensive sugar cane production and demand

in Brazil has led to deforestation of rain forests. Moreover, sugarcane fields in the state of

San Paulo are reported to cause air pollution due to pre-harvest burning (WWF, 2004).

Water pollution due to sugar cane industry and sugar cane agricultural practice (fertilizers

and pesticides) is another major environmental problem in Brazil (Gunkel et al., 2006).

75

Like other countries, India is also facing environmental problems due to sugar

cane cultivation. In the Indian state of Maharashtra, sugar cane irrigation uses 60% of the

total irrigation supply, which causes substantial groundwater withdrawals (WWF, 2004).

India’s largest river, the Ganges, experiences severe water stress. Sugar cane is one of the

major crops cultivated in the area and increases water scarcity (Gerbens-Leenes and

Hoekstra, 2009). Another problem resulting from sugar cane cultivation and sugar

processing activity in India is the pollution of surface and groundwater resources (Solomon,

2005).

Other ingredients and inputs

The results presented earlier in this chapter show that vanilla, which is part of the natural

flavour of our beverage, contributes largely to the overall water footprint (from 27% to

50%). The source of the vanilla is Madagascar, which is the main vanilla producing country

in the world. Cultivation of vanilla is one of the most labour-intensive agricultural crops

and it takes up to three years before the crop can be harvested. Harvested flowers need a

process called curing in order to take its aroma. This process needs heating of the vanilla

beans in hot water (65 degrees Celsius) for three minutes, which causes most environmental

problems in the production countries. Thermal pollution occurs as a result of hot water

discharged into freshwater systems, causing sudden increases in the temperature of the

ambient water systems above ecologically acceptable limits. In addition to water

contamination by means of temperature changes, the necessity of obtaining wood, the main

energy source of heating, causes deforestation of rainforests (TED, 2003).

Another small ingredient of our hypothetical beverage is caffeine. Although the

amount of caffeine used in the product is small, the water footprint is very high (53 litres

per bottle). The caffeine is sourced from coffee beans produced in Colombia, which is one

of the biggest coffee producers in the world. Two major problems exist in Colombia due to

coffee cultivation: loss of bird species and soil erosion. Additionally, pollution of surface

and ground water resources resulting from usage of fertilizers is a major environmental

problem due to coffee cultivation (TED, 2001).


The oil based materials used for the bottle of our beverage (PET-bottle, cap,

stretch films and labels) have particularly a grey water footprint. In PE production, large

amounts of water are used for cooling. Cooling water is considered as grey water as it

increases the temperature of the receiving freshwater bodies more than what is acceptable

from an ecological point of view. Water quality criteria for aquatic ecosystems indicate that

water temperature may not increase by more than a few degrees Celsius compared to

natural conditions (CEC, 1988). Additional freshwater sources are required to dilute hot

water stemming from cooling water (to decrease the temperature of discharged cooling

water in order to meet standards with respect to maximum increase of water temperature).

3.6 Conclusion

The total water footprint of our beverage is calculated as minimum 169 litres (using sugar

beet from the Netherlands) and maximum 309 litres (using sugar cane form Cuba). The

operational water footprint of the product is 0.5 litres, which forms 0.2-0.3% of the total

water footprint. The supply-chain water footprint constitutes 99.7-99.8% of the total water

footprint of the product.

The operational water footprint of the 0.5 litre PET-bottle sugar-containing

carbonated beverage consists of two components: the operational water footprint the

‘overhead water footprint’. The first is equal to the water incorporated into the product,

which is 0.5 litres. There is no other operational water footprint than this, because there is

no other water consumption or pollution in the factory related to the production of the

product. There is water use in the factory for general purposes such as flushing toilets,

cleaning working clothes, and washing and cooking in the kitchen, but all water used is

collected and treated in a public wastewater treatment plant before it is returned into the

environment. Thus, the net abstraction from the local water system for those activities is

zero.

The supply-chain water footprint of the product also consists of two components:

related to product inputs (ingredients and other inputs) and overhead. Most of the supply-

chain water footprint of the product is coming from its ingredients (95-97%). A smaller

77

fraction of the supply-chain water footprint comes from the other inputs (2-4%), mainly

from the PET-bottle. The overhead water footprint constitutes a minor fraction of the

supply-chain water footprint (0.2-0.3%).

The main impacts of the hypothetical product are stemming from the grey and blue

water footprints of the product. Ingredients like sugar, vanilla, caffeine (coffee) cause

contamination of natural freshwater sources (grey water footprint) because of the use of

fertilizers and pesticides. The biggest impact of the water footprint of the beverage is

related to the sugar ingredient. Many sugar producing countries are water-rich countries

where the water footprint does not relate to water stress. There are, though, several

localized hotspots, such as the sugar beet production in the Andalucía region in the South of

Spain, sugar cane production in Pakistan (Indus River) and India (Ganges River), and sugar

beet from Iran. With regard to water quality, pollution by nitrates is an issue in several

regions, such as the case of Northern France, Russia (Black Sea), India, Pakistan, Cuba,

Brazil, Iran and China. A rational N fertilization is important to reduce the environmental

impact of fertilization and to increase profitability in crop production. Better management

practices to reduce the environmental impacts in the sugar industry do not necessarily imply

reduced productivity and profits; indeed, measures to address environmental impacts can

provide economic benefits for farmers or mills through cost savings from more efficient

resource use. In addition, mostly sugar cane production relates to deforestation like in Cuba

and Brazil. Other negative effects of sugar production are impacts on biodiversity (decrease

of mangrove forestlands, and danger of extinction of the blind river dolphin in the Indus

Delta).

The results of this study show the importance of a detailed supply-chain

assessment in water footprint accounting. Common practice in business water accounting is

mostly restricted to the analysis of operational water use. This study shows that compared

to the supply-chain water footprint, the operational side is almost negligible. The results of

this study imply that compared to other water accounting tools, the concept of the water

footprint provides a more comprehensive tool for water accounting.


The study shows that the water footprint of a beverage product is very sensitive to

the production locations of the agricultural inputs. Even though the amount of sugar is kept

constant, the water footprint of our product significantly changes according to the type of

sugar input and production location of the sugar. Additionally, the type of water footprint

(green, blue and grey) changes according to location, which are mainly driven by the

difference in climatic conditions and agricultural practice in the production locations. These

results reveal the importance of the spatial dimension of water footprint accounting.

The study reveals that detailed and comprehensive supply-chain analysis is

essential for the calculation of the water footprint of a product. It shows that even small

ingredients can significantly affect the total water footprint of a product. On the other hand,

the study also shows that many of the components studied hardly contribute to the overall

water footprint.

The general findings of this study with respect to the ratio of operational to

supply-chain water footprint and the relative importance of ingredients, other inputs and

overhead can be extended to other beverages similar to our hypothetical beverage. The

major part of the water footprint of most beverages will be stemming from the supply

chain. The operational water footprint of the beverages similar to ours will be negligible

compared to the water footprint of the ingredients. This shows the importance of focusing

cooperate water policy towards to supply chain rather than operational water use.

This is the first study quantifying the overhead water footprint of a product.

Strictly spoken, this component is part of the overall water footprint of a product, but it was

unclear how relevant it was. This study reveals that the overhead component is not

important for this kind of studies and is negligible in practice.

By definition, the water footprint is a geographically explicit indicator, not only

showing volumes of water use and pollution, but also showing the various locations where

the water is used and the periods of the year in which the water is used (Hoekstra and

Chapagain, 2008). The question in practical applications is, however, whether it is feasible

to trace the precise locations and timing of water use in the supply chain of a product. In the

79

current water footprint study for a 0.5 litre PET-bottle sugar-containing carbonated

beverage we show that it is feasible to trace water use in the supply-chain relatively well,

based on a desk study only. Even better and more precise results could be obtained in a

more elaborate study including visits to the suppliers and finally to the farmers and mining

industries producing the primary ingredients. Knowing the blue, green and grey

components of the water footprint of a product and the precise locations and timing of

water use is essential for water footprint impact assessment, which in turn is key for

formulating mitigating policies. Accurate material flow accounting along the full supply-

chain of a product would simplify water footprint accounting.

4 Sustainability of national consumption from a water resources perspective: A case study for France3

Abstract

In recent years, it has become increasingly evident that local water depletion and pollution

are often closely tied to the structure of the global economy. It has been estimated that

twenty per cent of the water consumption and pollution in the world relates to the

production of export goods. For developing a wise national water policy, it is relevant to

consider the linkages between consumed goods in a country and impacts on freshwater

systems where the goods are produced. The objective of this study is to identify and

analyse how French water resources are allocated over various purposes, and to examine

where the water footprint of French production violates local environmental flow

requirements and ambient water quality standards. Additionally, the aim is to understand

the water dependency of French consumption and the sustainability of imports. The total

water footprint of production and consumption in France is 90 billion m3/year and 106

billion m3/year respectively. The blue water footprint of production is dominated by maize

production. The basins of the Loire, Seine, Garonne, and Escaut have been identified as

priority basins where maize and industrial production are the dominant factors for the blue

water scarcity. About 47% of the water footprint of French consumption is external and

related to imported agricultural products. Cotton, sugar cane and rice are the three major

crops with the largest share in France’s external blue water footprint of consumption and

identified as critical products in a number of severely water-scarce river basins. The basins

of the Aral Sea and the Indus, Ganges, Guadalquivir, Guadiana, Tigris & Euphrates, Ebro,

Mississippi and Murray rivers are some of the basins that have been identified as priority

basins regarding the external blue water footprint of French consumption. The study shows

that analysis of the external water footprint of a nation is necessary to get a complete

picture of the relation between national consumption and the use of water resources. It

provides understanding of how national consumption impacts on water resources elsewhere

in the world.

3 Based on Ercin et al. (2012b)

82/ Chapter 4. The water footprint of France

4.1 Introduction

Water plays a key role in life on our planet. It is essential not only for direct uses such as

for the provision of drinking water, growing food and the production of energy and other

products, but also for ensuring the integrity of ecosystems and the goods and services they

provide to humans. Freshwater is a renewable resource; however, its annual availability is

limited. Annual freshwater use in many places exceeds the limit of the water available,

which has resulted in river flows that are below environmental flow requirements, declining

groundwater levels and pollution of water bodies.

In recent years, it has become increasingly evident that local water depletion and

pollution are often closely tied to the structure of the global economy (Hoekstra and

Chapagain, 2007). It has been estimated that about twenty per cent of the water

consumption and pollution in the world relates to the production of export goods (Hoekstra

and Mekonnen, 2012a). International trade in commodities implies long-distance transfers

of water in virtual form, where virtual water is understood as the volume of water that has

been used to produce a commodity and that is thus virtually embedded in it (Chapagain and

Hoekstra, 2008). Knowledge about the virtual-water flows entering and leaving a country

can cast a new light on the actual water scarcity of a country. For developing a wise

national water policy, it is also relevant to consider the linkages between consumed goods

in a country and impacts on freshwater systems where the goods are produced.

The water footprint is an indicator of freshwater use that looks not only at direct

water use of a consumer or producer, but also at the indirect water use. The water footprint

can be regarded as a comprehensive indicator of freshwater resources appropriation, next to

the traditional and restricted measure of water withdrawal. It is a multi-dimensional

indicator, showing water consumption volumes by source and polluted volumes by type of

pollution; all components of a total water footprint are specified geographically and

temporally (Hoekstra et al., 2011).

The objective of this study is to carry out a water footprint assessment for France

from both a production and consumption perspective. The aim of the assessment from the

83

production perspective is to identify and analyse how French water resources are allocated

over various purposes, and examine where the water footprint of production within France

violates local environmental flow requirements and ambient water quality standards.

Additionally, the aim is to quantify which volumes of French water resources are allocated

for making products for export and to assess the impact related to this water footprint for

export. The assessment from the consumption perspective focuses on the analysis of the

external water footprint of French consumption, to get a complete picture of how national

consumption translates to water use, not only in France, but also abroad, and to assess

French dependency on external water resources and the sustainability of imports.

The study starts with a quantification and mapping of the water footprint of the

agricultural and industrial sectors and of domestic water supply within France. Next, virtual

water imports into France and virtual water exports leaving France are quantified, by traded

commodity. Subsequently, the internal and external water footprint of French consumption

is analysed. Finally, it has been analysed which components of the French blue water

footprints of production and consumption contribute to blue water scarcity in specific river

basins and which products are responsible herein.

There are several similar water footprint studies in the literature with a focus on a

specific country. Studies have been carried out, for example, for Belgium (Vincent et al.,

2011), China (Hubacek et al., 2009; Ma et al., 2006; Zhao et al., 2009), Germany

(Sonnenberg et al., 2009), India (Kampman et al., 2008), Indonesia (Bulsink et al., 2010),

the Netherlands (Van Oel et al., 2009), Spain (Garrido et al., 2010); and the UK (Chapagain

and Orr, 2008). These studies mainly focussed on the quantification of the water footprints,

were not based on a high-resolution spatial analysis and excluded an assessment of the

sustainability of the water footprint. Impacts of water footprints on a national scale are

partially addressed in Van Oel et al. (2009) for the Netherlands, Kampman et al. (2008) for

India and Chapagain and Orr (2009) for Spanish tomatoes. However, these studies lack

spatial detail as will employed in the current study, which will incorporate data on monthly

blue water scarcity at the level of river basins to assess how blue water footprints of

production and consumption contribute to water scarcity at river basin level.


From a methodological point of view, this study improves upon the previous

country-specific water footprint studies in three ways, following the global study by

Mekonnen and Hoekstra (2011b). First, the water footprints of production and consumption

are mapped at a high level of spatial detail. Second, the analysis explicitly includes green,

blue and grey water footprints. Finally, we make a substantial step beyond quantifying and

mapping the country’s water footprint of production and consumption by analysing how

different components in the water footprint may contribute to blue water scarcity in

different river basins and identifying which products are behind those contributions.

4.2 Method and data

4.2.1 Water footprint accounting

This study follows the methodology and terminology of water footprint assessment as

described in the Water Footprint Assessment Manual (Hoekstra et al., 2011). The water

footprint is an indicator of water use that looks at both direct and indirect water use of a

consumer or producer. The water footprint of an individual or community is defined as the

total volume of freshwater that is used to produce the goods and services consumed by the

individual or community. Water use is measured in terms of water volumes consumed

(evaporated or incorporated into the product) and polluted per unit of time. A water

footprint has three components: green, blue and grey. The blue water footprint refers to

consumption of blue water resources (surface and ground water). The green water footprint

is the volume of green water (rainwater) consumed, which is particularly relevant in crop

production. The grey water footprint is an indicator of the degree of freshwater pollution

and is defined as the volume of freshwater that is required to assimilate the load of

pollutants based on existing ambient water quality standards. The water footprint of

production and consumption in France is quantified according to the national water

footprint accounting scheme as shown in Figure 4.1.

85

Figure 4.1. The national water footprint accounting scheme (Hoekstra et al., 2011).

The ‘water footprint of national production’ refers to the total freshwater volume

consumed or polluted within the territory of the nation. This includes water use for making

products consumed domestically but also water use for making export products. It is

different from the ‘water footprint of national consumption’, which refers to the total

amount of water that is used to produce the goods and services consumed by the inhabitants

of the nation. This refers to both water use within the nation and water use outside the

territory of the nation, but is restricted to the water use behind the products consumed

within the nation. The water footprint of national consumption thus includes an internal and

external component. The internal water footprint of national consumption is defined as the

use of domestic water resources to produce goods and services consumed by the national

population. It is the sum of the water footprint within the nation minus the volume of

virtual-water export to other nations insofar as related to the export of products produced

with domestic water resources. The external water footprint of national consumption is

defined as the volume of water resources used in other nations to produce goods and

services consumed by the population in the nation considered. It is equal to the virtual-

water import into the nation minus the volume of virtual-water export to other nations

because of re-export of imported products.

Internal water footprint of

national consumption

External water footprint of

national consumption

+

Water footprint

of national consumption

Virtual water export related to

domestically made products

Virtual water re-export +

Virtual water export

+ + +

Water footprint

of national production

Virtual water import +

Virtual water budget


The water footprint of crops and derived crop products produced in France or

elsewhere were obtained from Mekonnen and Hoekstra (2010a, 2011a), who estimated the

global water footprint of crop production with a crop water use model at a 5 by 5 arc

minute spatial resolution. The water footprint of animal products that are produced in

France were taken from Mekonnen and Hoekstra (2010b, 2012). The data related to the

water footprint of production and consumption in France and the virtual water flows to and

from France were taken from Mekonnen and Hoekstra (2011b). In all cases, data refer to

the period 1996-2005.

4.2.2 Identifying priority basins and products

For the blue water footprint of French production and consumption, some

additional analysis was carried out in order to identify river basins of concern. After we

quantified and mapped the blue water footprints of French production and consumption, we

estimated which parts of both water footprints are situated in river basins with moderate to

severe water scarcity during part of the year. Monthly blue water scarcity values for the

major river basins around the world were taken from a recent global water scarcity study

(Hoekstra and Mekonnen, 2011; Hoekstra et al., 2012). The blue water scarcity values in

that study were calculated by taking the aggregated blue water footprint per basin and per

month over the blue water availability in that basin and month. The latter was taken as

natural runoff in the basin minus a presumptive standard for the environmental flow

requirement in the basin. They classified blue water scarcity values into four levels:

- low blue water scarcity (<100%): the blue water footprint is lower than 20% of natural

runoff and does not exceed blue water availability; river runoff is unmodified or

slightly modified; environmental flow requirements are not violated.

- moderate blue water scarcity (100-150%): the blue water footprint is between 20 and

30% of natural runoff; runoff is moderately modified; environmental flow

requirements are not met.

- significant blue water scarcity (150-200%): the blue water footprint is between 30 and

40% of natural runoff; runoff is significantly modified; environmental flow

requirements are not met.

87

- severe water scarcity (>200%): the monthly blue water footprint exceeds 40% of

natural runoff, so runoff is seriously modified; environmental flow requirements are

not met.

The following three criteria have been used to identify priority basins regarding

the various components of the blue water footprint of French production or consumption:

level of water scarcity over the year in the basin where the water footprint component is

located, the size of the blue water footprint of French production or consumption located in

the basin (agricultural and industrial products separately), and the significance of the

contribution of a specific product to the total blue water footprint in the basin in the scarce

month.

A specific river basin is identified as a ‘priority basin’ related to France's water

footprint of production or consumption of agricultural products if three conditions are

fulfilled: (a) the river basin experiences moderate, significant or severe water scarcity in

any specified period of the year; (b) the French blue water footprint of production or

consumption of agricultural products located in that basin is at least 1% of total blue water

footprint of production or consumption of agricultural products; and (c) the contribution of

any specific agricultural commodity to the total blue water footprint in that specific basin in

the period of scarcity is significant (more than 5%). In addition, a river basin is also

identified as a priority basin if the following two conditions are met: (a) the water scarcity

in the river basin is severe during part of the year; and (b) the contribution of any specific

agricultural commodity produced or consumed in France to the total blue water footprint in

that specific basin in the period of scarcity is very significant (more than 20%).

A river basin is identified as a priority basin related to France's water footprint of

production or consumption of industrial products if three conditions are fulfilled: (a) the

river basin experiences moderate, significant or severe water scarcity in any specified

period of the year; (b) the French blue water footprint of production or consumption of

industrial products located in that specific basin is at least 1% of the total water footprint of

production or consumption of industrial products; and (c) the contribution of industrial

activities to the total blue water footprint in that specific basin in the period of scarcity is


significant (more than 5%). In addition, a river basin is also identified as a priority basin if

the following two conditions are met: (a) the water scarcity in the river basin is severe

during part of the year; and (b) the contribution of industrial activities to the total blue

water footprint in that specific basin in the period of scarcity is very significant (more than

20%).

In addition to the quantitative analysis to identify priority basins and products

regarding the blue water footprint of French production and consumption, we assessed the

impacts of the grey water footprint of French production and consumption on a qualitative

basis.

4.3 Results

4.3.1 Water footprint of production

The total water footprint of national production in France is 90 Gm3/year for the

period 1996-2005, which is 1% of the total water footprint of production in the world

(Hoekstra and Mekonnen, 2012a). The largest part of this water footprint is green (76%),

followed by grey (18%) and blue (6%) (Table 4.1). Crop production constitutes the largest

share (82%) in the water footprint of national production in France, followed by industrial

activities (8%), grazing (6%), domestic water supply (3%) and livestock production (1%).

Among the crops, cereals contribute 47% to the total water footprint. Fodder crops (15%),

oil seed crops (9%) and fruits and nuts (6%) are the other major crop groups with a

significant share in the total water footprint (Figure 4.2). Crop production contributes 50%

to the total blue water footprint within France. The shares of industrial production, animal

water supply and domestic water supply in the blue water footprint are 26, 14 and 11%

respectively. In France, the grey water footprint is largely due to crop and industrial

production.

89

Table 4.1. The water footprint of national production in France (Mm3/year) by major

category.

Water footprint of crop production

Water footprint

of grazing

Water footprint of animal

water supply

Water footprint of industrial

production

Water footprint of domestic

water supply

Total water footprint

Green Blue Grey Green Blue Blue Grey Blue Grey Green Blue Grey

62700 2849 8018 5672 778 1488 5654 628 2221 68372 5743 15894

The spatial distributions of the green, blue and grey water footprint of national

production in France are shown in Figure 4.3. The water footprint per region is presented in

Figure 4.4. Centre region has the largest water footprint with 9.6 Gm3/year (12% of the

total). Other regions with a significant share are Midi-Pyrenees (7.6 Gm3/year), Poitou-

Charentes (6.7 Gm3/year), Champagne-Ardenne (5.5 Gm3/year), Aquitaine (5.4 Gm3/year),

Pays de la Loire (5.3 Gm3/year), Picardie (5 Gm3/year), Bourgogne (4.7 Gm3/year), and

Rhone-Alpes (4.2 Gm3/year). The largest blue water footprint in France is in Midi-Pyrenees

(where 14% of the blue water footprint within France is located). Aquitaine, Ile-de-France,

Centre, Poitou-Charentes, Pays de la Loire, Rhone-Alpes, Provence-Alpes-Cote d'Azur,

Languedoc-Roussillon are other regions with a large blue water footprint. The largest grey

water footprint in France is in Ile-de-France (where 10% of the grey water footprint within

France is located), followed by Centre (8%), Midi-Pyrenees (7.8%), Rhone-Alpes (7.3%),

Aquitaine (6.6%), Poitou-Charentes (6.4%), and Pays de la Loire (6%). The large grey

water footprint in Ile-de-France is due to the high population and industrial activity in the

region, especially near Paris metropolitan area.

The water footprint of agricultural production (crop production, grazing, and

livestock water supply) in the period 1996-2005 was 80 Gm3/year, which is 89% of the

total water footprint in France. Wheat (29%), fodder crops (18%), maize (14%), barley

(9%), rapeseed (7%), grapes (5%), sunflower (4%) and sugar beet (2%) are together

responsible for 88% of the total agricultural water footprint. Cauliflower, artichokes,

carrots, lettuce, asparagus, onions, cabbages and tomatoes are the major vegetables with


large water footprints. Among the fruits, the water footprint of grapes is the largest,

followed by apples, peaches and plumes.

Figure 4.2. The water footprint of national production in France by sector.

Table 4.2. The water footprint of national production in France (Mm3/year) in its major river basins.

River

basin

Total related to agricultural production

Related to industrial

production

Related to domestic

water supply Total water footprint*

Green Blue Grey Blue Grey Blue Grey Green Blue Grey Total

Loire 13868 606 1754 195 741 82 291 13868 884 2787 17538

Seine 12919 305 1531 389 1478 164 581 12919 858 3590 17367

Garonne 7113 746 1117 82 313 35 123 7113 863 1553 9530

Rhone 6325 329 729 221 836 94 332 6325 645 1896 8866

Rhine 3222 24 454 113 417 47 166 3222 184 1037 4444

Escaut 1256 24 161 58 221 24 86 1256 106 467 1829

Ebro 19 1 2 0 1 0 1 19 1 4 24

Po 5 0 0 0 2 0 1 5 1 3 9

* The water footprints within these major river basins sum up to 66 % of the total water footprint of

production in France.

Cereals 47%

Fruits and nuts 6%Oilseed crops 9%

Fodder 15%Grazing 6%Livestock 1%

Industry 8%

Domestic water supply 3%

Others 5%

Crops and grazing88%

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91

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rdenne

otprint

print of


The water footprint of industrial production in France in the period 1996-2005 was

7.1 Gm3/year. This footprint is dominated by the grey component (5.6 Gm3/year), which

represents the pollution due to industrial production. The water footprint of industrial

production is concentrated in the Seine (26%), Rhone (15%), Loire (13%), Rhine (7%) and

Garonne (6%) basins (Table 4.2). Ile-de-France, Rhone-Alpes, Provence-Alpes-Cote d'Azur

and Nord-Pas-de-Calais are the regions where water footprint of industrial production is

relatively large.

The water footprint of domestic water supply in France in the period 1996-2005

was 2.8 Gm3/year. The majority of it is grey water footprint (78%). This water footprint is

large where population concentrations are high and located mainly in Ile-de-France, Rhone-

Alpes and Provence-Alpes-Cote d'Azurb. From a river basin point of view: the Seine,

Rhone, Loire and Rhine basins, where most of the French population lives, have the largest

water footprint related to domestic water supply.

Figure 4.6. Spatial distribution of the water footprint of agricultural production in France.

4.3.2 Virtual water flows

The total virtual water import to France in the period 1996-2005 was 78.3 Gm3/year. About

73% of the virtual water imports relates to imported crops and crop products, 15% to

imported industrial products and 12% to imported animal products (Table 4.3). The largest

share (22%) of the total virtual water import relates to the import of cotton and its derived

pro

im

Fig

tot

im

oducts. Figure 4

mport, distinguishi

gure 4.7. The gre

The green

tal virtual water i

mported products,

4.7 shows the c

ing between gree

en, blue and grey

n water footprint

import. Cotton pr

, accountable for

ontribution of d

en, blue and grey

y virtual water imp

of imported pro

roducts have the

r 18% of the tota

different product

virtual water imp

port to France by

oducts is 52.7 Gm

largest green wat

al green virtual w

s to the virtual

ports.

y product group.

m3/year and is 6

ter footprint amo

water import. So

95

water

67% of

ong the

oybean


products (17%), animal products (14%), cocoa products (13%) and coffee products (11%)

are other products with a significant share in the green virtual water import. The blue water

footprint of imported products in France is 10.5 Gm3/year. Approximately 56% of this

footprint is due to cotton products. Animal and industrial products also have significant

shares in blue virtual water imports (9% each). The grey water footprint of imported

products is 15.1 Gm3/year. Industrial products give the largest contribution to this grey

water footprint (71%), followed by cotton products (13%) and animal products (4%).

The majority of the virtual water imports to France originate from Brazil (10%),

Belgium (9%), Spain (7%), Germany (7%), Italy (6%) and India (5%). Spain, Belgium,

Morocco, Italy, India, Uzbekistan, and Turkey are the largest blue virtual water exporters to

France, accounting for 55% of the blue virtual water import. The grey component of virtual

water import is mainly from China (10%), Germany (10%), Russia (10%), Italy (7%),

Belgium (7%), the USA (7%), Spain (5%) and India (4%). The green, blue and grey water

footprints of virtual water imports to France are shown in Figure 4.8.

The blue water footprint related to the total of imported cotton products is mainly

located in Uzbekistan, Turkey, India, Tajikistan, Turkmenistan and China. The blue water

footprint related to imported animal products mainly lies in Spain, Belgium, the

Netherlands, Germany and Italy. For industrial products, this ranking is: Germany (15%),

the USA (11%), China (9%), Italy (8%) and Russia (8%). Most of the grey water footprint

related to the import of industrial products lies in Russia (14%), China (11%), Germany

(10%) and the USA (7%).

The total virtual water export from France in the period 1996-2005 was 65.5

Gm3/year (Table 4.3). Since virtual water imports were larger than virtual water exports,

France is a net virtual water importer. The virtual water export is dominated by export of

crop products (69%) and followed by animal products (19%) and industrial products (12%).

The largest part of the virtual water export concerns green water (70%). The blue and grey

virtual water exports contribute 11 and 18% of total virtual water exports respectively.

97

Figure 4.8. Spatial distribution of the green, blue and grey water footprint of total virtual

water import to France.

98/

Ta

Im

E

Fig

Ge

(3%

fig

wh

Fra

com

8/ Chapter 4. The

able 4.3. Virtual w

Crop

Green B

mport 45.1 8

Export 35.9 4

gure 4.9. Green, b

The larges

ermany (11%), S

%) and Libya (3%

gure only shows v

heat products tog

ance. Barley, m

mmodities with

water footprint o

water import to Fra

p products

Blue Grey Gree

8.6 3.8 7.6

4.9 4.4 10.1

blue and grey virt

st virtual water fl

Spain (8%), the

%). Figure 4.9 sh

virtual water exp

gether are respon

maize, rapeseed

a large share in

of France

ance by product c

Animal products

en Blue Grey

6 0.9 0.6

1 1.5 0.8

tual water export

flows leaving Fra

United Kingdom

hows the virtual w

ports related to do

nsible for 54%

d, sunflower an

green virtual wa

category (Gm3/ye

Industrial products

Blue Grey

1.0 10.7

1.0 6.7

from France by p

ance go to Belgiu

m (7%), the Neth

water exports by

omestically made

of the green vir

nd grape produ

ater exports. Blu

ear).

Total

Green Blue

52.7 10.5

46.0 7.4

product group.

um (16%), Italy (

herlands (7%), A

product category

e products. Anim

rtual water flows

ucts are other

e virtual water e

Grey

15.1

12.0

(13%),

Algeria

y. This

mal and

s from

major

exports

99

from France are mainly due to the export of animal products (39%), industrial products

(26%) and maize products (17%). The largest grey virtual water export is due to the export

of industrial products (61% of the total) and is followed by maize, animal and barley

products.

4.3.3 Water footprint of consumption

The total water footprint of consumption in France is 106 Gm3/year over the period 1996-

2005. The green component is the largest and is equal to 76% of total water footprint of

consumption. Blue and grey water footprints of national consumption are 8 and 17% of the

total. About 53% of the water footprint of French national consumption is internal and 47%

is external (Table 4.4). This means that nearly half of the water resources consumed or

polluted to make all products consumed by French citizens are water resources outside the

country.

The largest fraction (87%) in the total water footprint of French consumers relates

to the consumption of agricultural products. Consumption of industrial products and

domestic water supply contribute 10% and 3% to the total water footprint of consumption,

respectively (Table 4.5). The internal water footprint of French consumption is mainly

because of the consumption of agricultural products, followed by industrial products and

domestic water supply (Figure 4.10). The external water footprint is largely due to the

import of agricultural products for domestic consumption, and for a smaller part due to the

import of industrial products. The ratio of external to total water footprint of consumption is

higher for industrial products (62%) than for agricultural products (47%). Furthermore, the

ratio of external to total water footprint is significantly higher for the blue water footprint

(64%) than for the green water footprint (46%) or the grey water footprint (47%). For

agricultural products, even 77% of the total blue water footprint of consumption is external.

Table 4.4. The internal and external water footprint of French consumption (Mm3/year).

Internal water footprint External water footprint Total water footprint Ratio of external

to total water footprint (%) Green Blue Grey Green Blue Grey Green Blue Grey

43704 2879 9295 36739 5156 8355 80443 8036 17649 47

10

Ta(M

G

4

tot

co

cer

res

in

cer

con

do

(8%

tot

con

(12

Fig

com

00/ Chapter 4. Th

able 4.5. The waMm3/year).

Water footprint of c

Internal

Green Blue Gr

3704 1375 37

With a co

tal water footpri

ffee, tea and coco

reals and sugar

spectively. Rubbe

the total water fo

reals have the l

nsumption (40,

minated by meat

%), milk (8%) an

tal blue water fo

nsumption of ind

2%) and milk (5%

gure 4.10. The to

mponent.

e water footprint

ater footprint of

consumption of agrproducts

Extern

rey Green Blu

753 36739 457

ontribution of 34

int of French co

oa (9%), and mil

contribute 5%

er, fruits, wine &

ootprint of consum

largest shares in

12, 10, 7 and

t consumption (2

nd domestic wate

ootprint. The grey

dustrial products

%).

tal water footprin

t of France

French consump

ricultural Watero

nal Int

e Grey Blue

77 2078 876

4%, meat consum

onsumption (Fig

lk (9%) are other

and 4% to the

& beer, and dome

mption. Meat, co

n the total gree

6% respectivel

23%). Consumpt

er supply (8%) ar

ey water footprin

(54%), followed

t of French consu

ption per major

r footprint of consumof industrial product

ernal Exte

Grey Blue

3320 579

mption is the lar

gure 4.11). Indu

large contributor

total water foot

estic water supply

offee-tea-cocoa, m

en water footprin

ly). The blue w

ion of industrial

re other sectors w

nt of consumption

d by domestic wat

umption shown by

consumption ca

mption ts

Water foof domwater srnal

Grey Blue

6277 628

gest contributor

ustrial products (

rs. The consumpt

tprint of consum

y each have a 3%

milk, vegetable oi

nt of French na

water footprint i

products (18%),

with a large share

n is mainly due

ter supply (13%)

y internal and ext

ategory

ootprint mestic supply

Grey

2221

to the

(10%),

tion of

mption,

% share

ils and

ational

s also

, fruits

e in the

to the

), meat

ternal

101

Meat34%

Industrial products10%

Coffee, tea, cocoa 9%

Milk9%

Vegetable Oils6%

Cereals5%

Rubber3%

Fruits 3% Domestic water

supply3%

Wine & beer3%

Sweetners2%

Offals2%

Sugar2%

Other9%

Other18%

Figure 4.11. The total water footprint of French consumption shown by consumption

category.

When we compare the external water footprint of France to virtual water imports,

we see that some part of the virtual water imports to France is not consumed domestically.

Around 35% of the virtual water import is re-exported again. Part of the re-export of

virtual-water is done after having processed imported raw materials. A typical example of

such processing is related to cotton and cocoa products. Crops are imported from Asia and

Latin America to be used as an input to textile and cocoa industries. When we compare the

internal water footprint of French consumption to the water footprint of production within

France, we see that the latter is much bigger. About 60% of the total water footprint of

production in France is for domestic consumption. The rest of the water footprint in the

country is for the production of export commodities.

The geographic distribution of the water footprint of consumption by French

citizens is shown in Figure 4.12. More than 50% of the external water footprint of French

consumption comes from Brazil, Belgium, Spain, Germany, Italy, India and the

Netherlands. The geographic spreading of the external water footprint related to the

consumption of agricultural and industrial products are different from each other. The

external agricultural water footprint is mainly from Brazil, Belgium, India, Spain, and

Germany, while the external industrial water footprint is more concentrated in China,

Russia, Germany and the USA.


Figure 4.12. The global water footprint of consumption by the inhabitants of France (period 1996-2005).

av

con

av

Gr

sm

foo

ind

Sw

Figco

Ta

P

(t

5

1

1

2

2

3

The water

erage, 1786 m3/y

nsumption per ca

erage, which is 1

reece have very l

mallest water foot

otprint of consu

dustrial products

witzerland.

gure 4.13. The guntries and the w

able 4.6. The wate

Population

thousands)

Inter

Gree

9436 735

0

500

1000

1500

2000

2500

3000

UK

Slovakia

r footprint of a

year (Table 4.6).

apita in France is

1385 m3/year (Fi

large water footp

tprints per capita

umers in Europe

s is especially

green, blue and world average (m3

er footprint of Fre

rnal water footprint

en Blue Grey

5 48 156

Poland

Germany

Nethe

rlands

G

consumer in Fra

Compared to oth

s below the avera

igure 4.13). Coun

prints per capita,

a in Europe. As c

is dominated b

high in countr

grey water footp3/year/cap).

ench consumption

External water f

Green Blue

618 87

Switzerland

Den

mark

Romania

Green Blue

ance in the perio

her EU countries

age. However, it

ntries like Portug

, whereas the UK

can be seen from

by agricultural p

ries like Belgiu

print of consump

n per capita (m3/y

footprint To

Grey Green

141 1353

France

Belgium

Malta

Grey

od 1996-2005 w

, the water footp

is more than the

gal, Spain, Cypru

K and Ireland ha

Figure 4.14, the

products. The sh

um, Luxembourg

ption per capita

year/cap).

otal water footprint

Blue Grey

135 297

Italy

Hun

gary

Spain

103

was, on

print of

world

us and

ave the

e water

hare of

g and

in EU

Total

1786

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urg

10

Fig

co

4.4

4.4

As

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1

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2

2

3

04/ Chapter 4. Th

gure 4.14. The wa

untries and the w

4 Priority bas

4.1 Water footp

s described in S

oduction and foll

ainly located in th

ese basins – the L

arcity at least on

onths in which

minate the water

e largest shares i

ater footprint in t

maller than for the

0

500

1000

1500

2000

2500

3000

UK

Slovakia

e water footprint

ater footprint of c

world average (m3

sins and produc

print of producti

Section 4.3, the

lowed by industry

he Loire, Seine, G

Loire, Seine, Gar

ne month a year

the moderate to

r footprint in the

n the blue water

the Escaut basin

e other three basin

Poland

Germany

Nethe

rlands

Agricultu

t of France

onsumption per c3/year/cap).

cts

ion

blue water footp

y and domestic w

Garonne, Rhone,

ronne and Escaut

r. Table 4.7 show

o severe water

ese months. The

r footprint of pro

is much smaller,

ns (Figure 4.15).

Switzerland

Den

mark

Romania

ure Industry

capita per consum

print of France

water supply. The

, Rhine and Esca

t – experience mo

ws, for each of

scarcity occurs

Loire, Seine and

duction in Franc

, but the area of t

France

Belgium

Malta

y Domestic

mption category in

is dominated by

e blue water footp

aut river basins. F

oderate to severe

these four basin

and the product

d Garonne basin

ce, 15% each. Th

this basin is also

Italy

Hun

gary

Spain

n EU

y crop

print is

Four of

e water

ns, the

ts that

s have

he blue

o much

Luxembo

urg

105

Table 4.7. Priority basins regarding the blue water footprint of production in France.

River basin Month Level of scarcity Products with significant contribution to the blue water footprint in the basin (% of contribution)

Loire August Significant Maize (58%), industrial production (6%)

September Significant Maize (45%), industrial production (10%)

Seine

July Moderate Industrial production (28%), maize (18%), domestic water supply (12%), potato (11%)

August Severe Maize (38%), industrial production (21%), domestic water supply (9%), potato (%7), sugar beet (%6)

September Severe Industrial production (28%), maize (27%), domestic water supply (12%)

October Moderate Industrial production (5 %), domestic (24%)

Garonne

July Moderate Maize (54%), soybean (1 %), fodder (5%)

August Significant Maize (59%), soybean (7%)

September Severe Maize (69%), soybean (8%)

Escaut

July Significant Industrial production (61%), domestic water supply (17%), potato (10%)

August Severe Industrial production (57%), domestic water supply (16%), maize (10%), potato (8%)

September Severe Industrial production (70%), domestic water supply (20%)

October Severe Industrial production (77%), domestic water supply (22%)

The Loire river basin experiences significant water scarcity in August and

September. The main activities contributing to the blue water footprint in this basin are

maize and industrial production. The Loire basin is considered an important farming area,

producing two thirds of the livestock and half of the cereal produced in France. The banks

of the river offer a habitat for a rich biodiversity. The river is a refuge for European

beavers, otters, and crested newts, and a migration route for fish such as Atlantic salmon.

The decrease in water levels in the river during the summer period has a negative effect on

the biodiversity located in the banks of the river (UNEP, 2004).

The Seine and Escaut river basins experience water scarcity from July to October.

The blue water footprint during this period in these basins is mainly because of industrial


production, domestic water supply, and maize and potato production. The Seine river

passes through Paris; the high level of urbanization and industrialization has a major impact

on the water quality in the basin. Pollution is due to industrial and domestic wastewater, but

also intensive agriculture. Agricultural production has a big impact on water quality

because it favours intensive farming techniques and spring crops, which leave the soil bare

for long periods of the year and increase the chemical load in the rivers by leaching and

draining. This has a harmful effect on both the environment and other water uses.

Improving water quality is still the major concern of the basin, where non-point source

pollution from farming and urban areas is still a major problem, as nitrate, pesticide and

heavy metal concentrations continue to increase (UNEP, 2004).

The Garonne faces moderate to severe water scarcity in the period from July to

September. The production of maize is the dominant factor behind the blue water scarcity

in this basin. Soybean and fodder are two other products that contribute significantly to the

blue water footprint in the basin. The Garonne is the most important river of south-western

France and main water source for five major cities, including Bordeaux. The Bordeaux

region is known for its industrial activities and is well known for the quality of its

vineyards. The region especially experiences water shortages during summertime

(UNESCO, 2006; AEAG, 2011). The Garonne is an important breeding area for sturgeon

and for the migration of Atlantic salmon. Its estuary, in particular, is a very important site

for fish and bird migrations. The water quality is worsening with wastewater from the city

of Bordeaux, causing high levels of nitrogen and phosphorous concentrations downstream

of Bordeaux. One tributary of the Garonne, the Dropt, is particularly sensitive to

eutrophication (Devault et al., 2007; UNEP, 2004). The pollution of a few heavy metals is

observed in the Garonne due to industrial activities, especially mining in the basin. This

contamination is considered as critical because of the sensitivity of the marine ecosystems

located at the downstream (Grousset et al., 1999).

107

Figure 4.15. Priority basins and products regarding the blue water footprint of production in

France.

A significant portion of the blue water footprint of production in France is for

production of export commodities. Around 60% of the agricultural blue water footprint and

40% of the industrial blue water footprint of production are not for producing commodities

for internal consumption but for production of export goods. Therefore, some of the

impacts of the water footprint of production in French river basins are due to consumption

happening elsewhere in the world but not in France.


The blue water footprint of French consumption is partly within France and partly outside.

In many of the basins where part of the water footprint of French consumption is located,

water scarcity is beyond hundred per cent during part of the year.

Agricultural products. We will focus first on the water footprint of French consumption of

agricultural products. Table 4.8 presents the river basins across the globe where there is a

significant blue water footprint related to French consumption of agricultural products and

where there is moderate, significant or severe water scarcity during part of the year. A

‘significant’ blue water footprint in a basin means here that at least 1% of the blue water


footprint of French consumption of agricultural products is located in this basin. The table

4.8 also shows a list of river basins where less than 1% of the blue water footprint of French

consumption of agricultural products is located. In these basins, water scarcity is severe

during part of the year (or even the full year) and the contribution of one or more specific

agricultural commodities to the total blue water footprint in the basin in the period of severe

scarcity is very significant (more than 20%). Although France imports this or these

products in relative small amounts (less than 1% of the blue water footprint of French

consumption of agricultural products is located in those basins), these products are

obviously contributing to very unsustainable conditions. Table 4.8 shows, per basin, the

number of months per year that the basin faces moderate, significant or severe water

scarcity, and priority products per basin. These priority products are the products that

contribute significantly to the basin’s blue water scarcity and are imported by France. The

basins listed in Table 4.8 are shown on the world map in Figure 4.16.

The Aral Sea basin is identified as one of the most important priority basins, since

6% of the blue water footprint of French consumption of agricultural products is located

there. The basin experiences one month of moderate water scarcity (June) and four months

of severe water scarcity (July to October). Cotton production is the dominant factor in the

blue water scarcity of the basin (more than 50%). Next in line of the priority basins are the

four French river basins that were already identified in the previous section as well: the

Garonne, Loire, Escaut and Seine basins. The blue water footprints within those basins lead

to moderate to severe water scarcity during parts of the year. For an important part, the blue

water footprints of production in these basins relate to producing for the domestic market.

A sixth priority basin is the Indus basin, in which 4% of the blue water footprint of French

consumption of agricultural products is located. The basin faces severe water scarcity

during eight months of the year. The blue water footprint in the Indus basin is mainly due to

wheat, cotton, rice and sugar cane production. However, wheat is not one of the products

that France imports from Pakistan, thus it is not a product of major concern for French

consumers.

109

Table 4.8. Priority basins regarding the blue water footprint of French consumption of

agricultural products.

River basin

Percentage of the blue

WF of French

consumption of

agricultural products located in this basin

Number of months per year that a basin faces moderate,

significant or severe water scarcity

Major contributing products

Moderate Significant Severe

Aral Sea basin 6.4 1 0 4 Cotton Garonne 5.4 1 1 1 Maize, soybean, animal products Escaut (Schelde) 4.5 0 1 3 Maize, potato Loire 4.4 0 2 0 Maize Indus 3.9 1 3 8 Cotton, rice, sugar cane Guadalquivir 3.0 1 0 6 Cotton, sun flower, rice, sugar beet Seine 2.2 2 0 2 Maize, potato, sugar beet Ganges 2.2 0 2 5 Rice, sugar cane Guadiana 1.8 1 0 6 Grapes, sunflower, citrus Tigris & Euphrates 1.6 0 1 5 Cotton, rice Po 1.6 2 0 0 Rice, animal products Ebro 1.4 0 0 3 Maize Sebou 1.4 1 1 5 Sugar beet Douro 1.3 2 0 3 Maize, sugar beet Tejo 1.0 1 0 4 Grapes, maize, animal products Mississippi 0.60 2 0 2 Maize, soybean, rice, cotton Krishna 0.45 1 1 7 Rice, sugar cane Godavari 0.31 2 0 5 Rice, sugar cane Kizilirmak 0.27 1 2 2 Sugar beet Chao Phraya 0.26 2 1 4 Rice, sugar cane Sakarya 0.25 0 1 5 Sugar beet Bandama 0.21 0 0 2 Sugar cane, animal products Cauvery 0.19 3 1 8 Rice, sugar cane Yongding He 0.12 0 0 12 Cotton, soybean Limpopo 0.11 2 0 5 Sugar cane, cotton Sacramento 0.10 1 0 5 Rice San Joaquin 0.10 1 0 7 Cotton, maize Sassandra 0.08 0 0 2 Sugar cane Comoe 0.08 0 0 2 Sugar cane Tapti 0.07 2 1 5 Cotton, sugar cane Murray 0.06 2 0 6 Sugar cane, cotton, rice Penner 0.04 1 2 9 Rice Incomati 0.03 1 0 3 Sugar cane Tugela 0.02 2 0 3 Grape, animal products Doring 0.01 0 1 7 Sugar cane, grapes Nueces 0.01 0 0 12 Maize


Figure 4.16. The river basins in the world in which the production of agricultural products for

French consumption contributes to moderate, significant or severe blue water scarcity.

The Ganges, Krishna, Godavari, Cauvery, Tapti and Penner basins are river basins

in India that are identified as priority basins. All these basins experience severe water

scarcity during most of the year. Rice and sugar cane production are the major reasons of

blue water scarcity in these basins. The Guadalquivir, Guadiana, Douro and Tejo are

Spanish-Portuguese river basins in which the blue water footprint of French consumption is

significant. Sugar beet, maize, grapes, citrus and sunflower are the products that are

imported by France and contribute largely to the blue water footprint in these basins.

As can be seen from Table 4.8, mainly eight agricultural products of concern are

identified in 36 different priority basins: cotton, rice, sugar cane, sugar beet, soybean, maize

and grape. Among them, cotton, sugar cane and rice are the three major crops. They have

the largest share in the external blue water footprint of French consumption and are

111

identified as products of concern in most of the priority basins. Therefore, we examined

impacts of these three products in some of the identified priority basins in detail.

Cotton. Cotton is probably the most important product if it comes to the contribution of

French consumers to blue water scarcity. French cotton consumption relates to blue water

scarcity in a number of basins throughout the world: the Aral Sea basin (Uzbekistan), the

Indus (Pakistan), the Guadalquivir (Spain and Portugal), the Tigris & Euphrates

(originating in Turkey and ending in Iraq), the Mississippi (USA), the Yongding He

(China), the Limpopo (South Africa), the San Joaquin (USA), the Tapti (India), and the

Murray (Australia). The Aral Sea ecosystem has been experiencing sudden and severe

ecosystem damage due to excessive water abstractions from the inflowing rivers to irrigate

cotton fields and other export crops. This unsustainable use of water has environmental

consequences, including fisheries loss, water and soil contamination, and dangerous levels

of polluted airborne sediments. The impacts of extensive irrigation in the Aral Sea basin has

extended far beyond the decline of the sea water level: millions of people lost access to the

lake’s water, fish, reed beds, and transport functions. Additionally, environmental and

ecological problems associated with extensive water use for irrigation negatively affected

human health and economic development in the region (Cai et al., 2003; Glantz, 1999;

Micklin, 1988). Another well-documented case is the Murray basin in Australia, where

water levels have declined significantly, particularly due to water abstractions for irrigation.

Much of its aquatic life, including native fish, are now declining, rare or endangered

(Chartres and Williams, 2006).

Sugar cane. Sugar cane is the second product if it comes to the contribution of French

consumers to blue water scarcity in the world. Sugar cane consumed in France contributes

to water scarcity in the following priority basins: the Indus (Pakistan), the Ganges (India),

the Krishna (India), the Godavari (India), the Chao Phraya (Thailand), the Bandama (Côte

d'Ivoire), the Cauvery (India), the Limpopo (South Africa), the Sassandra (Côte d'Ivoire),

the Comoe (Côte d'Ivoire), the Tapti (India), the Murray (Australia), the Incomati (South

Africa) and the Doring (South Africa). The freshwater reaching to Indus delta has

significantly decreased (90%) as a result of over-usage of water sources in the Indus basin.

Sugar cane is one of the main water consuming agricultural products in the basin. The


decrease in freshwater flow to the Indus delta has negative impacts on the ecosystems and

biodiversity of the delta (such as decrease of mangrove forestlands and danger of extinction

of the Blind River Dolphin). Additionally, excessive water usage in sugar cane cultivation

areas has led to salinity problems (WWF, 2004). Moreover, untreated wastewater discharge

from sugar mills causes depletion of available oxygen in water sources, which threatens

fish and other aquatic life (Akbar and Khwaja, 2006). India is also facing environmental

problems due to sugar cane cultivation. In the Indian state of Maharashtra, sugar cane

irrigation is 60% of the total irrigation supply, which causes substantial groundwater

withdrawals (WWF, 2004). India’s largest river, the Ganges, experiences severe water

scarcity. Sugar cane is one of the major crops cultivated in the area and deteriorates the

water scarcity. Another problem resulting from sugar cane cultivation and sugar processing

activity in India is the pollution of surface and groundwater resources (grey water footprint)

(Solomon, 2005).

Rice. Rice has the third largest share in the external blue water footprint of French

consumption. In the following priority basins, rice is identified as one of the major products

contributing to blue water scarcity: the basins of the Indus (Pakistan), Guadalquivir (Spain),

Ganges (India), Tigris & Euphrates (Turkey to Iraq), Mississippi (USA), Krishna (India),

Godavari (India), Chao Phraya (Thailand), Cauvery (India), Sacramento (USA) and Murray

(Australia). The Guadalquivir is Spain’s second longest river. Its natural environment is one

of the most varied in Europe. Its middle reaches flow through a populous fertile region

where its water is used extensively for irrigation. The lower course of the Guadalquivir is

used for rice cultivation. In recent years, mass tourism and intensive irrigated agriculture in

the region are causing over-exploitation of regional aquifers, which damages the ecosystem

of the region (UNEP, 2004). The Guadalquivir marshes are negatively affected due to

agricultural activities. The Guadalquivir is classified as one of the rivers in Europe mostly

polluted due to non-point source emissions from agricultural activities (nitrate and

phosphate) (Albiac and Dinar, 2008).

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Industrial products. There are two river basins that face moderate to severe water scarcity

during part of the year and where more than 1% of the blue water footprint of French

consumption of industrial products is located: the Seine and the Escaut basins (Table 4.9).

There are seven river basins where this contribution is smaller, but that can be classified as

priority basin for another reason. These river basins are the basins of the Volga, St.

Lawrence, Ob, Wisla, Don, Yongding He and Colorado. In these basins, water scarcity is

severe during part of the year or even the full year, as in the case of the Yongding He

(Table 4.9). Although France imports industrial products from these basins in relative small

amounts (less than 1% of the blue water footprint of French consumption of industrial

products is located in those basins), these products contribute to very unsustainable

conditions because industrial products contribute more than 20% to the total blue water

footprint in the basin in the period of severe scarcity.

Table 4.9. Priority basins regarding the blue water footprint of French consumption of industrial products.

River basin

Percentage of the blue water footprint of French consumption of industrial products located in this

basin

Number of months per year that a basin faces moderate, significant

or severe water scarcity

Moderate Significant Severe

Seine 5.5 2 0 2

Escaut (Schelde) 1.5 0 1 3

Volga 0.43 0 0 1

St. Lawrence 0.31 0 0 1

Ob 0.23 1 0 1

Wisla 0.14 0 0 1

Don 0.10 0 2 2

Yongding He 0.09 0 0 12

Colorado (Caribbean Sea) 0.01 1 0 6

Industrial products contribute to pollution as well. France’s industrial grey water

footprint is located mainly in the Seine, Loire, Rhone, Escaut, Garonne, Volga, Mississippi,

Po, St. Lawrence, Tigris & Euphrates, Ob, Huang He (Yellow River) and Yangtze basins.


China's longest river, the Yangtze, has been severely polluted. The surface water pollution

in the river includes industrial and domestic sewage, animal manures, chemical fertilizers

from farmlands, and polluted sediments. The Yellow River in China is known for pollution

problems as well. According to Chinese government estimates, around two-thirds of the

Yellow River's water is too polluted to drink. Around 30% of fish species in the river are

believed to have become extinct and the river's fish catch has declined by 40% (Fu et al.,

2004).

4.5 Discussion and conclusion

This study of the water footprint of national production and consumption for France is more

detailed than previous national water footprint studies (that were carried out for other

countries). It builds on the high-resolution global water footprint study by Hoekstra and

Mekonnen (2012a) by zooming in on one particular country. The availability of the global

study enabled us to map in a relatively precise way the external water footprint of French

consumption. The study could make use of another recent study on global blue water

scarcity (Hoekstra et al., 2012) to identify which parts of the French external water

footprint are located in river basins that experience moderate to severe blue water scarcity

during part of the year. The data that are thus generated can play a role in revisiting French

national water policy. Linking specific consumer products in a country to water problems

elsewhere is still uncommon in governmental thinking about water policy. Making this link

visible can help in setting priorities in either national or international context with respect to

the most effective measures to reduce water footprints in the basins where most needed.

The study addresses questions like: where and when water footprints are largest, where and

when they contribute most to local water scarcity and which specific products contribute

most to water footprints and water scarcity? By making the links between specific

consumer products and water problems visible, the study suggests that consumer product

policy can be part of a water policy. The extent to which French government is willing to

promote water footprint reductions in water-scarce basins and periods of the year through

product-oriented policies is obviously a political question. This study shows how a political

debate on this topic could be informed by relevant knowledge on how different products

contribute to water scarcity.

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Even though the study applies higher spatial and temporal resolutions than

previous national water footprint studies, there are still limitations regarding the spatial and

temporal detail, which primarily relate to lacking crop and irrigation data on even higher

resolutions and to the problem of tracing supply chains and trade flows. One limitation in

the study is that the origin of virtual water imports and the external water footprint of

consumption have not been traced further than the first tier trade partners. If a product is

imported from a country, we assume that the product has been produced in that country and

we take the water footprint of the imported product accordingly. Another limitation related

to trade data is that the origins of imported commodities are available on country level and

not specified as per river basin or in even more geographic detail. In this study, we assumed

that an imported product originates from the various river basins within the country

proportionally to the production of that product in the various basins.

Another limitation in the study pertains to the problem of distinguishing between

different industrial products. Different crop and animal products have been considered

separately, but industrial commodities are treated as one product group. In future studies it

would be worth trying to analyse different industrial sectors and commodities separately;

currently, the major challenge still is the lack of water consumption and pollution data per

industrial sector and the complexity of supply chains for many industrial commodities.

In this study, identification of priority river basins and priority products from the

perspective of water resource use has been done primarily on the basis of data on the levels

of blue water scarcity through the year on a river basin level. More precise results would be

obtained if we could use water scarcity data on a finer spatial resolution level, for example

at the level of sub-catchments. Especially for identifying hotspots within large river basins,

this would be very helpful. Furthermore, by looking at ‘blue water scarcity’ from an

environmental point of view, we may have neglected social issues of water conflict. For

obtaining a more complete overview of potential critical basins and products, it would be

helpful to look at other indicators than environmental water scarcity alone. It should further

be noted that the blue water scarcity estimates used in this study (from Hoekstra and

Mekonnen, 2011; Hoekstra et al., 2012) excluded the evaporation from storage reservoirs

and the effect of inter-basin water transfers. This may result in an underestimation of blue


water scarcity in basins with significant evaporation from large reservoirs and export of

water to another basin and an overestimation of water scarcity in basins that receive

significant volumes of water from another basin. The water scarcity estimates also exclude

storage effects of large dams, which means that water scarcity may have been

underestimated in periods of the year in which water is being stored and overestimated in

periods of the year in which the water is being released. Finally, we used a number of

criteria to identify priority basins, with certain thresholds (like the threshold of ‘at least 1%

of the total blue water footprint should be located in the basin’) that can be considered as

subjective choices. Obviously, changing thresholds will lead to longer or shorter lists of

‘priority basins’.

Despite the limitations of the study, it has been proven that it is possible to make a

rough sketch of where different economic sectors contribute to scarcity within the country

and of which consumer goods contribute to water scarcity in specific river basins outside

the country. The study shows that analysis of the external water footprint of a nation is

necessary to get a picture of how national consumption depends on foreign water resources.

5 Water footprint scenarios for 2050: A global analysis and case study for Europe4

Abstract

This study develops water footprint scenarios for 2050 based on a number of drivers of

change: population growth, economic growth, production/trade pattern, consumption

pattern (dietary change, bioenergy use) and technological development. Our study

comprises two assessments: one for the globe as a whole, distinguishing between 16 world

regions, and another one for Europe, whereby we zoom in to the country level. The

objective of the global study is to understand the changes in the water footprint of

production and consumption for possible futures by region and to elaborate the main drivers

of this change. In addition, we assess virtual water flows between the regions of the world

to show dependencies of the regions on water resources in the other regions under different

possible futures. In the European case study, our objective is to assess the water footprint of

production and consumption at country level and Europe’s dependence on water resources

elsewhere in the world. We constructed four scenarios, which are structured along two

axes, representing two key dimensions of uncertainty: globalisation versus regional self-

sufficiency, and economy-driven development versus development driven by social and

environmental objectives. The two axes create four quadrants, each of which represents a

scenario: global markets (S1), regional markets (S2), global sustainability (S3) and regional

sustainability (S4).

The WF of production in the world in 2050 has increased by 130% in S1 relative

to the year 2000. In S2, the WF of production shows and increase of 175%, in S3 30% and

in S4 46%. Among the scenarios, S1 and S2 have a higher WF of production as the world

consumes more animal-based products. Scenario S2 yields the largest WF of production

due to a larger population size and a higher demand in biofuels than S1. When the world

food consumption depends less on animal products (S3 and S4), the increase in the WF of

production becomes less. The WF of consumption in the world increases by +130% relative

4 Based on Ercin and Hoekstra (2012a)

118/ Chapter 5. Water footprint scenarios for 2050

to 2000 for the S1 scenario. It increases by +175% in S2, +30% in S3 and +46% in S4. The

high increase in the WF of consumption for S1 and S2 can, for a significant part, be

explained by increased meat consumption. When we compare trade liberalization (S1 and

S3) to self-sufficiency scenarios (S3 and S4), it is observed that trade liberalization

decreases the WF of consumption globally. The world average WF of consumption per

capita increases by +73% in S1, +58% in S2, -2% in S3 and 10% in S4 compared to 2000

volumes.

The total WF of production in Western Europe increases by +12% in S1 and +42%

in S2 relative to 2000 values. It decreases 36% in S3 and 29% in S4. Eastern Europe

increases its WF of production by +150% and +107% in S1 and S2 compared to 2000,

respectively. The increase is lower in S3 and S4 than in the other scenarios, but volumes are

36% and 31% higher than in 2000, respectively. The total WF of consumption in WEU

increases by 28% and 52% in S1 and S2 compared to 2000. The WF of consumption in

WEU decreases by -19% in S3 and -20% in S4. EEU increases its WF of consumption in

all scenarios compared to 2000, by +143% in S1, +75% in S2, +17% in S3 and +20% in S4.

The WF of consumption per capita in WEU increases by +30% in S1 and +22% in S2 and

decreases by -18% in S3 and -28% in S4. EEU has a higher WF of consumption per capita

in 2050 than 2000 with an increase of 186% in S1, 57% in S2, 38% in S3 and 23% in S4.

This study shows how different driver will change the level of water consumption

and pollution globally in 2050. These estimates can form an important basis for a further

assessment of how humanity can mitigate future freshwater scarcity. We showed with this

study that reducing humanity’s water footprint to sustainable levels is possible even with

increasing populations, provided that consumption patterns change. This study can help to

guide corrective policies at both national and international levels, and to set priorities for

the years ahead in order to achieve sustainable and equitable use of the world’s fresh water

resources.

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5.1 Introduction

Availability of freshwater in sufficient quantities and adequate quality is a prerequisite for

human societies and natural ecosystems. In many parts of the world, excessive freshwater

consumption and pollution by human activities put enormous pressure on this availability

as well as on food security, environmental quality, economic development and social well-

being. Competition over freshwater resources has been increasing during decades due to a

growing population, economic growth, increased demand for agricultural products for both

food and non-food use, and a shift in consumption patterns towards more meat and sugar

based products (Shen et al., 2008; Falkenmark et al., 2009; De Fraiture and Wichelns, 2010;

Strzepek and Boehlert, 2010). It looks like today’s problems related to freshwater scarcity

and pollution will be aggravated in the future due to a significant increase in demand for

water and a decrease in availability and quality. Many authors have estimated that our

dependency on water resources will increase significantly in the future and this brings

problems for future food security and environmental sustainability (Rosegrant et al., 2002;

Alcamo et al., 2003a; Bruinsma, 2003; 2009; Rosegrant et al., 2009). A recent report

estimates that global water withdrawal will grow from 4,500 billion m3/year today to 6,900

billion m3/year by 2030 (McKinsey, 2009).

Scenario analysis is a tool to explore the long-term future of complex socio-

ecological systems under uncertain conditions. This method can and indeed has been used

to assess possible changes to global water supply and demand. Such studies have been an

interest not only of scientists but also of governmental agencies, businesses, investors and

the public at large. Many reports have been published to assess the future status of water

resources since the 1970s (Falkenmark and Lindh, 1976; Kalinin and Bykov, 1969; Korzun

et al., 1978; L'vovich, 1979; Madsen et al., 1973; Schneider, 1976). Water scenario studies

address changes in future water availability and/or changes in future water demand. Some

of the recent scenario studies focused on potential impacts of climate change and socio-

economic changes on water availability (e.g. Arnell, 1996, 2004; Milly et al., 2005; Fung et

al., 2011). Other scenario studies also included the changes in water demand (Alcamo et al.,

1996; Seckler, 1998; Alcamo et al., 2000; Shiklomanov, 2000; Vörösmarty et al., 2000;

Rosegrant et al. 2002, 2003; Alcamo et al., 2003a, b, 2007).


The major factors that will affect the future of global water resources are:

population growth and concentration, economic growth, changes in production and trade

patterns, increasing competition over water because of increased demands for domestic,

industrial and agricultural purposes and the way in which different sectors of society will

respond to increasing water scarcity and pollution. These factors are also mentioned in

World Water Scenarios to 2050, a preparatory study on how to construct the upcoming

generation of water scenarios by UNESCO and the United Nations World Water

Assessment Programme (Cosgrove and Cosgrove, 2012; Gallopín, 2012). In this study, ten

different drivers of change are identified as critical to assess water resources in the long-

term future: demography, economy, technology, water stocks, water infrastructure, climate,

social behaviour, policy, environment and governance.

In this study, we focus on water demand scenarios. In Table 5.1, we compare the

scope of the current study with other recent water demand scenario studies. Vörösmarty et

al. (2000) estimated agricultural, industrial and domestic water withdrawal for 2025,

distinguishing single trajectories for population growth, economic development and change

in water use-efficiency. The analysis was carried out at a 30′ grid resolution. Shiklomanov

(2000) assessed water withdrawals and water consumption for 26 regions of the world for

the year 2025. He projected agricultural, industrial and municipal water use considering

population, economic growth and technology change (water efficiency). Another global

water scenario study was undertaken by Rosegrant et al. (2002; 2003), who addressed

global water and food security for the year 2025. They studied irrigation, livestock,

domestic and industrial water withdrawal and consumption in 69 river basins under three

main scenarios. Compared to other recent studies, their study includes the most extensive

list of drivers of change: population growth, urbanization, economic growth, technology

change, policies and water availability constraints. Technology change was addressed in

terms of irrigation efficiency, domestic water use efficiency and growth in crop and animal

yields. Policy drivers included water prices, water allocation priorities among sectors,

commodity price policy as defined by taxes and subsidies on commodities. Climate change

effects on water demand were not included in the study, but three alternative water

availability constraints were included. Changes in food demand, production and trade were

121

estimated for each scenario based on the drivers distinguished. The effect of increased

biofuel consumption was not included. Different trajectories were considered for each

driver, except for the economic and demographic drivers. Population growth, the speed of

urbanization and economic growth were held constant across all scenarios. Alcamo et al.

(2003a) analysed the change in water withdrawals for future business-as-usual conditions in

2025 under the assumption that current trends in population, economy and technology

continue. They studied changes in water withdrawal at a 0.5° spatial resolution. A more

recent assessment by Alcamo et al. (2007) improved their previous study by distinguishing

two alternative trajectories for population and economic growth, based on the A2 and B2

scenarios of the IPCC for the years 2025, 2055 and 2075. Shen et al. (2008) studied

changes in water withdrawals in the agricultural, industrial and domestic sectors for the

years 2020, 2050 and 2070. They addressed socio-economic changes (population, GDP,

water use efficiency) as described in four IPCC scenarios (A1, A2, B1 and B2),

disaggregating the world into 9 regions. One of the most extensive water demand scenario

studies was done by De Fraiture et al. (2007) and De Fraiture and Wichelns (2010). These

studies focused on alternative strategies for meeting increased demands for water and food

in 2050. They elaborated possible alternatives under four scenarios for 115 socio-economic

units (countries and country groups). Their analysis distinguished water demand by green

and blue water consumption. The agriculture sector was analysed considering 6 crop

categories and livestock separately. The industrial sector was schematized into the

manufacturing industry and the thermo-cooling sector. Many drivers were addressed

explicitly, like food demand, trade structure, water productivity, change in water policies

and investments, in addition to the conventional drivers of population and economic

growth. Despite covering most of the critical drivers, they excluded effects of climate

change and biofuel demand from their study. Most of these studies have paid little

attention to the fact that, in the end, total water consumption and pollution relate to the

amount and type of commodities we consume and to the structure of the global economy

and trade, that supplies the various consumer goods and services to us. None of the global

scenario studies addressed the question of how alternative consumer choices influence the

future status of the water resources except Rosegrant et al. (2002; 2003). In addition, the


links between trends in consumption, trade, social and economic development have not yet

been fully integrated.

Table 5.1. Comparison of existing global water demand scenarios with the current study.

Study Study characteristics Sectoral disaggregation

Drivers used to estimate future water demand (no. of trajectories distinguished)

Vörösmarty et al. (2000)

Time horizon: 2025 Spatial scale: 30′ grid resolution Scenarios: 1 Scope: Blue water withdrawal

Agriculture Industry Domestic

Population growth (1) Economic growth (1) Technology change (1)

Shiklomanov (2000)

Time horizon: 2025 Spatial scale: 26 regions Scenarios: 1 Scope: Blue water withdrawal and consumption



Rosegrant et al. (2002; 2003)

Time horizon: 2025 Spatial scale: 69 river basins Scenarios: 3 Scope: Blue water withdrawal and consumption

Agriculture: 16 sub-sectors Industry Domestic

Population growth (1) Urbanization (1) Economic growth (1) Technology change (3) Policies (3) Water availability constraints (3)

Alcamo et al. (2003a)

Time horizon: 2025 Spatial scale: 0.5° spatial resolution Scenarios: 1 Scope: Blue water withdrawal



Alcamo et al. (2007)

Time horizon: 2025/2055/2075 Spatial scale: 0.5° spatial resolution Scenarios: 2 Scope: Blue water withdrawal



Shen et al. (2008)

Time horizon: 2020/2050/2070 Spatial scale: 9 regions Scenarios: 4 Scope: Blue water withdrawal



De Fraiture and Wichelns (2010)

Time horizon: 2050 Spatial scale: 115 socio-economic units Scenarios: 4 Scope: Green and blue water consumption

Agriculture: 7 sub-sectors Industry: 2 sub-sectorsDomestic

Population growth (1) Economic growth (1) Production and trade patterns change (4) Technology change (4) Consumption patterns - diet (1)

Current study

Time horizon: 2050 Spatial scale: 227 countries, 16 regions Scenarios: 4 Scope: Green and blue water consumption, pollution as grey water footprint

Agriculture: 20 sub-sectors Industry Domestic

Population growth (3) Economic growth (4) Production and trade patterns change (4) Technology change (2) Consumption patterns - diets (2) Consumption patterns – biofuel (3)

123

The current study develops water footprint scenarios for 2050 based on a number

of drivers of change: population growth, economic growth, production/trade pattern,

consumption pattern (dietary change, bioenergy use) and technological development. It

goes beyond the previous global water demand scenario studies by a combination of

factors: (i) it addresses blue and green water consumption instead of blue water withdrawal

volumes; (ii) it considers water pollution in terms of the grey water footprint; (iii) it

analyses agricultural, domestic as well as industrial water consumption; (iv) it

disaggregates consumption along major commodity groups; (v) it integrates all major

critical drivers of change under a single, consistent framework. In particular, integrating all

critical drivers is crucial to define policies for wise water governance and to help policy

makers to understand the long-term consequences of their decisions across political and

administrative boundaries.

We have chosen in this study to look at water footprint scenarios, not at water

withdrawal scenarios as done in most of the previous studies. We explicitly distinguish

between the green, blue and grey water footprint. The green water footprint refers to the

consumptive use of rainwater stored in the soil. The blue water footprint refers to the

consumptive use of ground or surface water. The grey water footprint refers to the amount

of water contamination and is measured as the volume of water required to assimilate

pollutants from human activities (Hoekstra et al., 2011).

Our study comprises two assessments: one for the globe as a whole, distinguishing

between 16 world regions, and another one for Europe, whereby we zoom in to the country

level. The objective of the global study is to understand the changes in the water footprint

of production and consumption for possible futures by region and to elaborate the main

drivers of this change. In addition, we assess virtual water flows between the regions of the

world to show dependencies of the regions on water resources in the other regions under

different possible futures. In the European case study, our objective is to assess the water

footprint of production and consumption at country level and Europe’s dependence on

water resources elsewhere in the world.


5.2 Method

5.2.1 Scenario description

For constructing water footprint scenarios, we make use of global scenario exercises of the

recent past as much as possible. This brings two main advantages: building our scenarios on

well-documented possible futures and providing readers quick orientation of the storylines.

As a starting point, we used the 2×2 matrix system of scenarios developed by the IPCC

(Nakicenovic et al., 2000). These scenarios are structured along two axes, representing two

key dimensions of uncertainty: globalisation versus regional self-sufficiency, and economy-

driven development versus development driven by social and environmental objectives.

The two axes create four quadrants, each of which represents a scenario: global markets

(S1), regional markets (S2), global sustainability (S3) and regional sustainability (S4)

(Figure 5.1). Our storylines resemble the IPCC scenarios regarding population growth,

economic growth, technological development and governance. For the purpose of our

analysis, we had to develop most of the detailed assumptions of the scenarios ourselves, but

the assumptions were inspired from the storylines of the existing IPCC scenarios. The

scenarios are consistent and tell reliable stories about what may happen in future. It is

important to understand that our scenarios are not predictions of the future; they rather

show alternative perspectives on how water footprints may develop towards 2050.

Figure 5.1. The four scenarios distinguished in this study.

Globalization

Regional self‐sufficiency

Environmental sustainability

Economy driven development

Global market (S1)

Global sustainability (S3)

Regional markets (S2)

Regional sustainability (S4)

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First, we constructed a baseline for 2050, which assumes a continuation of the

current situation into the future. The four scenarios were constructed based on the baseline

by using different alternatives for the drivers of change. The baseline constructed for 2050

assumes the per capita food consumption and non-food crop demand as in the year 2000. It

also considers technology, production and trade as in the year 2000. Economic growth is

projected as described in IPCC scenario B2. Climate change is not taken into account. The

increase in population size is taken from the medium-fertility population projection of the

United Nations (UN, 2011). Therefore, changes in food and non-food consumption and in

the water footprint of agriculture and domestic water supply are only subject to population

growth. The industrial water footprint in the baseline depends on economic growth.

Scenario S1, global market, is inspired by IPCC’s A1 storyline. The scenario is

characterized by high economic growth and liberalized international trade. The global

economy is driven by individual consumption and material well-being. Environmental

policies around the world heavily rely on economic instruments and long-term

sustainability is not in the policy agenda. Trade barriers are gradually removed. Meat and

dairy products are important elements of the diet of people. A rapid development of new

and efficient technologies is expected. Energy is mainly sourced from fossil fuels. Low

fertility and mortality are expected.

Scenario S2, regional markets, follows IPCC’s A2 storyline. It is also driven by

economic growth, but the focus is more on regional and national boundaries. Regional self-

sufficiency increases. Similar to S1, environmental issues are not important factors in

decision-making, new and efficient technologies are rapidly developed and adopted, and

meat and dairy are important components in the diets of people. Fossil fuels are dominant,

but a slight increase in the use of biofuels is expected. Population growth is highest in this

scenario.

Scenario S3, global sustainability, resembles IPCC’s B1 storyline. The scenario is

characterized by increased social and environmental values, which are integrated in global

trade rules. Economic growth is slower than in S1 and S2 and social equity is taken into

consideration. Resource efficient and clean technologies are developed. As the focus is on


environmental issues, meat and dairy product consumption is decreased. Trade becomes

more global and liberalized. Reduced agro-chemical use and cleaner industrial activity is

expected. Population growth is the same as for S1.

Scenario S4, local sustainability, is built on IPCC’s B2 storyline and dominated by

strong national or regional values. Self-sufficiency, equity and environmental sustainability

are at the top of the policy agenda. Slow long-term economic growth is expected. Personal

consumption choices are determined by social and environmental values. As a result, meat

consumption is significantly reduced. Pollution in the agricultural and industrial sectors is

lowered. Biofuel use as an energy source is drastically expanded.

These scenarios are developed for 16 different regions of the world for the year

2050. We used the country classification and grouping as defined in Calzadilla (2011a).

The regions covered in this study are: the USA; Canada; Japan and South Korea (JPK);

Western Europe (WEU); Australia and New Zealand (ANZ); Eastern Europe (EEU);

Former Soviet Union (FSU); Middle East (MDE); Central America (CAM); South America

(SAM); South Asia (SAS); South-east Asia (SEA); China (CHI); North Africa (NAF); Sub-

Saharan Africa (SSA) and the rest of the world (RoW). The composition of the regions is

given in Appendix 5.1.

5.2.2 Drivers of change

We identified five main drivers of change: population growth, economic growth,

consumption patterns, global production and trade pattern and technology development.

Table 5.2 shows the drivers and associated assumptions used in this study.

Population growth

Changes in population size are a key factor determining the future demand for goods and

services, particularly for food items (Schmidhuber and Tubiello, 2007; Godfray et al., 2010;

Kearney, 2010; Lutz and KC, 2010). The IPCC scenarios (A1, A2, B1, and B2) used

population projections from both the United Nations (UN) and the International Institute for

Applied Systems Analysis (IIASA). The lowest population trajectory is assumed for the A1

127

and B1 scenario families and is based on the low population projection of IIASA. The

population in the A2 scenario is based on the high population projection of IIASA. IPCC

uses UN’s medium-fertility scenario for B2. We used UN-population scenarios (UN, 2011)

for all our scenarios: the UN high-fertility population scenario for S2, the UN medium-

fertility population scenario for S4 and the UN low-fertility population scenario for S1and

S3. Population forecasts per region are given in Appendix 5.2.

Table 5.2. Drivers and assumptions per scenario.

Driver

Scenario S1:

Global market

Scenario S2:

Regional markets

Scenario S3:

Global sustainability

Scenario S4:

Regional sustainability

Population growth Low-fertility High-fertility Low-fertility Medium fertility

Economic growth* A1 A2 B1 B2

Consumption patterns

Diet Western high meat Western high meat Less meat Less meat

Bio-energy demand

Fossil-fuel domination

Biofuel expansion

Drastic biofuel expansion

Drastic biofuel expansion

Global production and trade pattern

Trade liberalization (A1B+ TL2)

Self-sufficiency (A2+SS1)

Trade liberalization (A1B+TL1)

Self-sufficiency (A2+SS2)

Technology development

Decrease in blue water footprints in agriculture

Decrease in blue water footprints in agriculture

Decrease in green and grey water footprints in agriculture

Decrease in blue and grey water footprints in industries and domestic water supply

Decrease in green and grey water footprints in agriculture

Decrease in blue and grey water footprints in industries and domestic water supply

*The scenario codes refer to the scenarios as used by the IPCC (Nakicenovic et al., 2000)


Economic growth

We assumed that the water footprint of industrial consumption is directly proportional to

the GDP. We used GDP changes as described in IPCC scenarios A1, A2, B1, and B2 for

S1, S2, S3 and S4, respectively. The changes in GDP per nation are taken from the database

of the Center for International Earth Science Information Network of Columbia University

(CIESIN, 2002).

Consumption patterns

We distinguished two alternative food consumption patterns based on Erb et al. (2009b):

- ‘Western high meat’: economic growth and consumption patterns accelerate in the

coming decades, leading to a spreading of western diet patterns. This scenario

brings all regions to the industrialised diet pattern.

- ‘Less meat’: each regional diet will develop towards the diet of the country in the

region that has the highest calorie intake in 2000, but only 30% of the protein

comes from animal sources.

We used the ‘western high meat’ alternative for S1 and S2 and the ‘less meat’ for

S3 and S4. Erb et al. (2009b) provide food demand per region in terms of kilocalories per

capita for 10 different food categories: cereals; roots and tubers; pulses; fruits and

vegetables; sugar crops; oil crops; meat; pigs, poultry and eggs; milk, butter and other dairy

products; and other crops. We converted kilocalorie intake per capita to kg/cap by using

conversion factors taken from FAO for the year 2000 (FAO, 2012). We also took seed and

waste ratios per food category into account while calculating the total food demand in 2050.

Per capita consumption patterns for fibre crops and non-food crop products were

kept constant as it was in 2000. It is assumed that the change in demand for these items is

only driven by population size. Per capita consumption values are taken from FAOSTAT

for the year 2000 (FAO, 2012).

129

We integrated three different biofuel consumption alternatives into our scenarios.

We used biofuel consumption projections as described by Msangi et al. (2010). They used

the International Model for Policy Analysis of Agricultural Commodities and Trade

(IMPACT) to estimate biofuel demand for 2050 for three different alternatives:

- Baseline: Biofuel demand remains constant at 2010 levels for most of the

countries. This scenario is a conservative plan for biofuel development. This is

used in S1.

- Biofuel expansion: In this scenario, it is assumed that there will be an expansion in

biofuel demand towards 2050. It is based on current national biofuel plans. This is

applied in S2.

- Drastic biofuel expansion: Rapid growth of biofuel demand is foreseen for this

scenario. The authors developed this scenario in order to show the consequences

of going aggressively for biofuels. This option is used for the S3 and S4 scenarios.

Msangi et al. (2010) provide biofuel demand in 2050 in terms of crop demands for

the USA, Brazil and the EU (Table 5.3). We translated their scenarios to the regions as

defined in our study by using the biofuel demand shares of nations for the year 2000. The

demand shares are taken from the US Energy Information Administration (EIA, 2012).

Table 5.3. Biofuel demand in 2050 for different scenarios (in tons.)

Crop Region Baseline Biofuel expansion Drastic biofuel iCassava World 660,000 10,640,000 21,281,000

Maize EU 97,000 1,653,000 3,306,000 USA 35,000,000 130,000,000 260,000,000 RoW 2,021,000 30,137,000 60,274,000

Oil seeds Brazil 16,000 197,000 394,000 EU 1,563,000 18,561,000 37,122,000 USA 354,000 3,723,000 7,447,000 RoW 530,000 5,172,000 10,344,000

Sugar Brazil 834,000 14,148,000 28,297,000 USA 265,000 5,840,000 11,680,000 RoW 163,000 2,785,000 5,571,000

Wheat EU 1,242,000 15,034,000 30,067,000 RoW 205,000 3,593,000 7,185,000

Source: Msangi et al. (2010).


Global production and trade pattern

The regional distribution of crop production is estimated based on Calzadilla et al. (2011a),

who estimated agricultural production changes in world regions by taking climate change

and trade liberalization into account (Appendix 5.4). They used a global computable

general equilibrium model called GTAP-W for their estimations. The detailed description

of the GTAP-W and underpinning data can be found in Berrittella et al. (2007) and

Calzadilla et al. (2010; 2011b). In their study, trade liberalization is implemented by

considering two different options:

- Trade liberalization 1 (TL1): This scenario assumes a 25% tariff reduction for all

agricultural sectors. In addition, they assumed zero export subsidies and a 50%

reduction in domestic farm support.

- Trade liberalization 2 (TL2): It is a variation of the TL1 case with 50% tariff

reduction for all agricultural sectors.

In addition, Calzadilla et al. (2011a) elaborated potential impacts of climate

change on production and trade patterns considering IPCC A1B and A2 emission scenarios.

In total, they constructed 8 scenarios for 2050 considering two climate scenarios (A1B and

A2), two trade liberalization scenarios (TL1 and TL2) and their combinations (A1B+TL1,

A1B+TL2, A2+TL1, A2+TL2). For the S1 and S3 scenarios, we considered production

changes as estimated in A1B+TL2 and A1B+TL1 respectively. We used the A2 for the S2

and S4 scenarios but we also introduced self-sufficiency options to S2 and S4 as described

below:

- Self-sufficiency (SS1): This alternative assumes 20% of reduction in import of

agricultural products (in tons) by importing regions compared to the baseline in

2050. Therefore, exporting regions are reducing their exports by 20%. This is

applied in S2.

- Self-sufficiency (SS2): In this alternative, we assumed 30% reduction in imports

by importing nations relative to the baseline in 2050. This option is used for S4.

131

Technology development

The effect of technology development is considered in terms of changes in water

productivity in agriculture, wastewater treatment levels and water use efficiencies in

industry. For scenarios S3 and S4, we assumed that the green water footprints of crops get

reduced due to yield improvements and for scenarios S1 and S2 we assumed that the blue

water footprints of crops diminish as a result of improvements in irrigation technology. We

assigned a percentage decrease to green and blue water footprints for each scenario based

on the scope for improvements in productivity as given in De Fraiture et al. (2007), who

give levels of potential improvement per region in a qualitative sense. For scenarios S1 and

S2 we assume reductions in blue water footprints in line with the scope of improved

productivity in irrigated agriculture per region as given by De Fraiture et al. (2007). For

scenarios S3 and S4 we assume reductions in green water footprints in line with the scope

for improved productivity in rainfed agriculture per region, again taking the assessment by

De Fraiture et al. (2007) as a guideline. For scenarios S3 and S4 we took reductions in grey

water footprints similar to the reductions in green water footprints. To quantify the

qualitative indications of reduction potentials in De Fraiture et al. (2007), we assigned a

reduction percentage of 20% to ‘some’ productivity improvement potential, 30% to ‘good’

productivity improvement potential and 40% for ‘high’ productivity improvement potential.

To reflect improvements in wastewater treatment levels and blue water use

efficiencies, we applied a 20% reduction in the blue and grey water footprints of industrial

products and domestic water supply in S3 and S4.

5.2.3 Estimation of water footprints

This study follows the terminology of water footprint assessment as described in the Water

Footprint Assessment Manual (Hoekstra et al., 2011). The water footprint is an indicator of

water use that looks at both direct and indirect water use of a consumer or producer. Water

use is measured in terms of water volumes consumed (evaporated or incorporated into the

product) and polluted per unit of time. The water footprint of an individual or community is

defined as the total volume of freshwater that is used to produce the goods and services

consumed by the individual or community. The ‘water footprint of national (regional)


production’ refers to the total freshwater volume consumed or polluted within the territory

of the nation (region). This includes water use for making products consumed domestically

but also water use for making export products. It is different from the ‘water footprint of

national (regional) consumption’, which refers to the total amount of water that is used to

produce the goods and services consumed by the inhabitants of the nation (region). This

refers to both water use within the nation (region) and water use outside the territory of the

nation (region), but is restricted to the water use behind the products consumed within the

nation (region). The water footprint of national (regional) consumption thus includes an

internal and external component. The internal water footprint of consumption is defined as

the use of domestic water resources to produce goods and services consumed by the

national (regional) population. It is the sum of the water footprint of the production minus

the volume of virtual-water export to other nations (regions) insofar as related to the export

of products produced with domestic water resources. The external water footprint of

consumption is defined as the volume of water resources used in other nations (regions) to

produce goods and services consumed by the population in the nation (region) considered.

It is equal to the virtual-water import minus the volume of virtual-water export to other

nations (regions) because of re-export of imported products.

5.2.3.1 Water footprint of agricultural consumption and production

Regional consumption of food items

The food consumption , in ton/year related to commodity group c in region r in the

year 2050 is defined as:

, , / (5.1)

where is the population in region r in 2050 and , the daily

kilocalorie intake per capita related to commodity group c in region r in this year. The

coefficient / is the conversion factor from kcal/cap/day to ton/cap/year, which is

obtained from FAO (2012). Population and kcal values per region for the year 2050 are

obtained from UN (2011) and Erb et al. (2009b), respectively.

133

Regional consumption of fibres and other non-food items

The fibre and other non-food consumption , , ton/year, related to commodity group

c in region r in 2050 is defined as:

, ∑ , | (5.2)

where , | is the per capita demand for commodity group c in nation n

that is located in region r, in 2000, which is obtained from FAO (2012).

Regional consumption of biofuel

Crop use for biofuels , , in ton/year, related to commodity group c in region r in 2050

is defined as:

, ∑ | (5.3)

where is the crop use for biofuels in 2050 regarding commodity group c,

taken according to one of the scenarios as defined in Msangi et al. (2010), and f n |

the energy crop share in 2000 of nation n that is located in region r is taken from EIA

(2012).

Global consumption

Total consumption for each commodity group in the world, in ton/year, is calculated as:

∑ , (5.4)

∑ , (5.5)

∑ , (5.6)

Total production

We assume that, per commodity group, total production meets total consumption:


(5.7)

(5.8)

(5.9)

Production shares of the regions

The expected production , (ton/year) related to commodity group c in region r is

defined as the multiplication of the production share , of region r and the total

production of commodity group c in the world.

, , (5.10)

Production shares of the regions per scenario are taken from Calzadilla et al. (2011a).

Trade

The surplus , (ton/year) related to commodity group c in region r is defined as the

difference between in production p and consumption c:

, , , (5.11)

Net import i (ton/year) per commodity group and per region is equal to the

absolute value of the surplus if s is negative. Similarly, net export e is equal to the surplus if

s is positive:

, | |, 00, 0 (5.12)

, 0, 0, 0 (5.13)

Trade, T (tons/year) of commodity group c, from exporting region re to importing

region ri is estimated as:

135

, , , , (5.14)

where , refers to the amount of import of commodity group c by importing

region ri and fe to the export fraction of exporting region re, which is calculated as the share

of export of region re in the global export of commodity group c.

Unit water footprint per agricultural commodity groups per region

The unit water footprint, , (m3/ton), of commodity group c produced in region r is

calculated by multiplying the unit WF of the commodity group in 2000 with a factor, α, to

account for productivity increase:

, , | (5.15)

The factor α is determined per scenario as described in Section 5.2.2. The values

taken for α are presented in Appendix 5.3. The unit water footprints of commodities per

region in 2000 are obtained from Mekonnen and Hoekstra (2010a; b).

Water footprint of agricultural production

The water footprint of production related to commodity group c in region r is calculated as:

, , , , (5.16)

Virtual water flows

The net virtual water flow VW (m3/year) from exporting region re to importing

region ri as a result of trade in commodity group c is calculated by multiplying the

commodity trade , , between the regions and the unit water footprint , of

the commodity group in the exporting region:

, , , , , , (5.17)


Water footprint of consumption of agricultural commodities

The water footprint of consumption ( , , , Mm3/year) related to the

consumption of commodity group c in region r is calculated as the water footprint of

production of that commodity, , , in region r plus the net virtual-water import to

the region related to that commodity.

, , , ∑ , , (5.18)

5.2.3.2 Water footprint of industrial consumption and production

Water footprint of consumption of industrial commodities

The water footprint related to the consumption of industrial commodities ( , ,

Mm3/year) in region r in 2050 is calculated by multiplying the water footprint of industrial

consumption in 2000 by the growth in GDP and a factor β representing productivity

increase (see Section 5.2.2).

, ∑ , (5.19)

The water footprint related to consumption of industrial commodities in nation n

in 2000 is taken from Mekonnen and Hoekstra (2011b). GDP changes are taken from

CIESIN (2002).

Water footprint of industrial production

The water footprint of industrial production ( , , Mm3/year) in region r in 2050 is

calculated by multiplying the global water footprint of industrial consumption in 2050 by

the share of the water footprint of industrial production of region r in the global water

footprint of industrial production in 2000.

, ∑ , , ∑ ,

(5.20)

137

The water footprint of industrial production per region r in 2000 is taken from

Mekonnen and Hoekstra (2011b).

5.2.3.3 Water footprint of domestic water supply

The water footprint of domestic water supply per region in 2050, (Mm3/year), is

calculated by multiplying the population in 2050 with the water footprint of domestic water

supply per capita in 2000 and factor β representing productivity increase:

∑ , (5.21)

The data for the water footprint of domestic water supply in 2000 are taken from

Mekonnen and Hoekstra (2011b).

5.2.4 European case study

In the global study, Europe is described by two regions: Western and Eastern Europe. To

enable us to make a more detailed analysis for Europe, we use country specific data on

population change and per capita food consumption for Western and Eastern Europe. We

down-scaled the results obtained for Western and Eastern Europe to the nations within

Europe. To estimate production, trade, virtual water flows, and water footprint of

production and consumption per country within Europe, we followed the same

methodology as described in the 5.2.3. The regions in the equations are replaced by the

nations of Europe. The production distribution among the European countries in 2050 is

done by taking the production patterns in 2000 (FAO, 2012).

5.3 Global water footprint in 2050

5.3.1 Water footprint of production

The WF of production in the world in 2050 has increased by 130% in S1 relative to the year

2000 (Table 5.4). In S2, the WF of production shows and increase of 175%, in S3 30% and

in S4 46%. The increase in the total WF of production is highest for industrial products in

S1 (600%). This increase is less for the other scenarios as they have a lower increase in

GDP than S1. The WF of agricultural production is higher in S1 and S2 (112 and 180%


more than 2000 values) than in S3 and S4 (18 and 38% more than 2000), which is due to

dietary differences between S1/S2 and S3/S4. Among the scenarios, S2 has the largest WF

of production as it has the highest population and high meet consumption. The WF of

production related to domestic water supply increases by 18% in S1, 55% in S2, -6% in S3

and 9% in S4.

In 2000, approximately 91% of the total WF of production is related to agricultural

production, 5% to industrial production and 4% to domestic water supply. The WF of

industrial production increases its share in the total for the S1, S2 and S4 scenarios.

In all scenarios, the WF of production is dominated by the green component.

However, the share of the green component decreases from 76% in 2000 to 74% in 2050 in

S1 (Figure 5.2). The share of the blue component decreases from 10% in 2000 to 7% in

2050 in S1. The grey WF increases its share from 14% in 2000 to 19% in S1. The shares of

the green, blue and grey WF of production in S2 are 82, 7, and 11% respectively. The share

of the green component falls down to 68 and 69% in S3 and S4, while an increase is

observed in the share of blue WF.

Among the scenarios, S1 and S2 have a higher WF of production as the world

consumes more animal-based products. Scenario S2 yields the largest WF of production

due to a larger population size and a higher demand in biofuels than S1. When the world

food consumption depends less on animal products (S3 and S4), the increase in the WF of

production becomes less.

Among the regions, SAM and ANZ show the highest increase in the total WF of

production in S1. The increase in ANZ is 217% for S1, 251% for S2, 54% for S3 and 33%

for S4. The increase is quite significant for SAM as well (361, 422, 168, and 144% for S1,

S2, S3 and S4, respectively). SSA increases its water footprint of production 181% in S1,

364% in S2, 81% in S3 and 184% in S4. The USA, CAM, Canada, SEA, EEU, FSU, MDE,

NAF and SAS are the other regions, which have a higher WF of production in 2050

compared to 2000 in all scenarios.

139

The WF of JPK’s production decreases for all scenarios. The change is -46% for

S1, -21% for S2, -68% for S3 and -55% for S4. This relates to the fact that JPK increasingly

externalizes its WF of consumption towards 2050. The WF of production in WEU increases

in S1 and S2 by 12 and 42%, respectively, but decreases for S3 and S4, by 36 and 29%

relative to 2000 values. The main reason for the decrease in S3 and S4 is due to dietary

preferences, shifting from high to low meat content. Despite the increase in the WF of

production in China in S1 and S2 (by 137 and 129%), a decrease is observed in S3 (6%).

Table 5.4. Percentage change in the water footprint of production compared to 2000. ‘A’

refers to WF of agricultural production, ‘D’ refers to WF of domestic water supply, ‘I’ refers to

WF of industrial production and ‘T’ refers to total WF.

Region S1 S2 S3 S4

A D I T A D I T A D I T A D I T

USA 105 24 16 87 154 57 20 128 49 -1 -9 38 59 12 -13 46

Canada 139 26 57 118 193 58 44 161 84 1 37 70 80 13 18 66

WEU 19 -3 -45 12 51 22 -28 42 -34 -23 -57 -36 -28 -13 -46 -29

JPK -52 -20 -16 -46 -24 1 -15 -21 -75 -36 -31 -68 -60 -28 -34 -55

ANZ 221 40 -75 217 255 77 -50 251 55 12 -77 54 34 26 -57 33

EEU 50 -24 833 150 85 0 274 107 -17 -39 393 36 -17 -30 355 31

FSU 46 -18 1,649 135 83 10 531 105 -12 -34 735 30 -11 -24 529 19

MDE 40 44 208 46 157 88 80 151 1 15 122 5 78 32 41 74

CAM 143 21 341 142 204 63 127 196 37 -3 198 39 44 13 142 45

SAM 372 24 474 361 441 66 158 422 172 -1 262 168 149 15 160 144

SAS 67 38 1,160 84 149 85 353 150 -10 11 1,495 16 25 28 653 36

SEA 127 32 953 151 191 76 257 188 32 6 458 45 37 22 400 49

CHI 89 -12 1,885 137 127 16 338 129 -22 -29 555 -6 -22 -19 967 6

NAF 32 43 533 44 81 90 236 85 2 14 651 17 27 32 112 29

SSA 179 122 863 181 367 183 243 364 78 78 649 81 184 101 335 184

RoW 114 -9 71 106 195 11 12 177 12 -27 -11 9 34 -20 110 36

World 112 18 601 130 180 55 158 175 18 -6 311 30 38 9 261 46

The WF of industrial production shows a drastic increase relative to 2000 for CHI,

FSU and SAS in S1. Industrial WFs in these regions increase by a factor of more than 10

times, up to 18 times for CHI. Other regions with high industrial WF increase in S1 are

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5.3.2 Virtual water flows between regions

Net virtual water import per region for each scenario is given in Table 5.5. The regions

WEU, JPK, SAS, MDE, NAF and SSA are net virtual water importers for all scenarios in

2050. The USA, Canada, ANZ, EEU, FSU, CAM, SAM, SEA and CHI are net virtual

water exporters in 2050.

All net virtual water-exporting regions in 2000 stay net virtual water exporters in

all 2050 scenarios. Net virtual water export from these regions increases in S1 and S2

compared to 2000, except Canada and SEA. SAM, FSU and the USA substantially

increase their net virtual water export in S1 and S2. SAM becomes the biggest virtual water

exporter in the world in 2050 for all scenarios and increases its net virtual water export

around 10 times in S1 and S2. The change is also high in S3 and S4 with an increase by a

factor 6 and 5, respectively. Another region that will experience a significant increase in net

virtual water export is the FSU. Compared to 2000, the net virtual water flow leaving this

region becomes 9 times higher in S1, 6 times in S2 and S3, and 4 times in S4. The net

virtual water export from the USA increases by a factor 3 in both S1 and S2 relative to

2000. The net virtual export from the USA decreases in S3 and S4 compared to 2000.

Although Canada continues to be a net virtual water exporter in 2050, its virtual water

export decreases below the levels of 2000 for S1, S3 and S4. Despite still being a net virtual

water exporter in 2050, SEA experience a decrease in the net virtual water export volumes

compared to 2000 in all scenarios.

All net virtual water-importing regions in 2000 stay net virtual water importers in

2050 for all scenarios except CAM and CHI, which become net virtual water exporters in

2050. The net virtual water import by WEU stays below the 2000 volume for S2 and S4.

Although JPK has a slightly higher net virtual water import in S1 and S2 than 2000, it

decreases its net virtual water import for the other scenarios. SSA is the region where the

highest increase in virtual water import is observed in 2050. Its net virtual water import

rises drastically in S1 and S2 compared to 2000. Other regions with a significant increase in

net virtual water import are MDE and SAS. The net virtual water import is the highest in S1

for all importing regions except SAS and NAF. WEU shows a different pattern, where the

143

net virtual water import is the highest in S3. The reason behind this is the significant

increase in biofuel demand in WEU in S3.

The regions show similar patterns for the virtual water flows related to trade crop

products. For the virtual water flows related to trade in animal products, this is slightly

different. The USA, Canada, WEU, ANZ, EEU, FSU, CAM, SAM and CHI are net virtual

water exporters and JPK, MDE, SAS, SEA, NAF and SSA are net virtual water importers

regarding trade in animal products.

Table 5.5. Net virtual water import per region (Gm3/year). ‘A’ refers to the net virtual water

import related to agricultural products, ‘I’ to the net virtual water import related to industrial

products and ‘T’ to the total net virtual water import.

2000 S1 S2 S3 S4

A I T A I T A I T A I T A I T

USA -117 27 -91 -377 92 -284 -350 48 -303 -101 57 -44 -101 39 -62

Canada -42 -1 -43 -43 4 -39 -48 1 -47 -37 2 -35 -31 2 -29

WEU 59 43 102 3 101 104 6 60 66 42 70 112 24 38 61

JPK 90 9 99 89 22 111 89 11 100 55 15 71 43 9 52

ANZ -72 3 -70 -140 5 -134 -154 3 -151 -102 4 -97 -82 2 -80

EEU -8 -2 -10 -59 46 -13 -63 3 -60 -46 11 -35 -36 15 -21

FSU -9 -34 -43 -183 -198 -381 -200 -77 -277 -150 -109 -259 -119 -56 -174

MDE 20 5 25 416 50 465 402 14 416 261 30 291 198 11 209

CAM 14 3 18 -127 41 -86 -117 11 -106 -83 23 -60 -59 12 -48

SAM -174 1 -173 -1,695 34 -1,661 -1,736 6 -1,730 -1,007 15 -992 -801 10 -792

SAS 232 -8 224 1,056 14 1,070 1,117 -12 1,105 625 -29 596 509 7 515

SEA -191 -12 -203 -146 -33 -179 -149 -16 -165 -140 -25 -166 -102 -11 -113

CHI 116 -38 78 -171 -244 -415 -152 -66 -218 -101 -103 -204 -63 -97 -159

NAF 60 0 60 66 14 80 84 3 87 47 11 59 46 3 49

SSA 3 1 4 1,249 20 1,269 1,223 3 1,226 720 12 732 564 6 569

RoW 21 3 24 60 31 92 49 8 56 15 14 29 10 11 21

The net virtual water flows related to industrial products in 2050 have a

completely different structure. The USA, Canada, WEU, JPK, ANZ, EEU, MDE, CAM,


SAM, NAF and SSA are the virtual water importers and FSU, SEA and CHI are net virtual

water exporters related to trade of industrial products in all scenarios. SAS is a net virtual

water importer in S1 and S4 and a net virtual water exporter in S2 and S3 regarding trade of

industrial products. Most of the virtual water export related to industrial products comes

from considering industrial products. In all regions, both net virtual water imports and

exports are the highest in the S1 scenario regarding trade of industrial products as this

scenario foresees the highest GDP increase and trade liberalization. Interregional virtual

water trade related to industrial products decreases from S2 to S4. The decrease in S2 is due

to increased self-sufficiency among the regions and the decrease in S3 and S4 is mainly due

to improvements in water use efficiency and wastewater treatment in the industry sector.

Regarding interregional blue virtual water flows, the USA, ANZ, FSU, CAM,

SAM and CHI are the net exporters and Canada, JPK, SAS and SSA are the net importers

in all scenarios and in 2000. Despite being a net blue virtual importer in 2000, WEU

becomes a net blue virtual water exporter in S2 and S4. NAF, a net blue virtual water

importer in 2000, becomes a net blue virtual water exporter in S1 and S2. In all scenarios,

the biggest net blue virtual water importers are SSA and SAS, whereas the biggest net blue

virtual water exporters are SAM and CHI.

CHI and FSU are the biggest net virtual water exporting regions in terms of the

grey component. Other net exporting regions are Canada, SEA, SAM and ANZ, for all

scenarios. The USA, WEU, JPK, MDE, CAM, SAS, NAF and SSA are the net grey virtual

water importing regions in all scenarios. EEU is a net importer of grey virtual water in S1,

S3 and S4 but a net exporter in S2.


The WF of consumption in the world increases by +130% relative to 2000 for the S1

scenario. It increases by +175% in S2, +30% in S3 and +46% in S4 (Table 5.6). The high

increase in the WF of consumption for S1 and S2 can, for a significant part, be explained by

increased meat consumption. When we compare trade liberalization (S1 and S3) to self-

145

sufficiency scenarios (S3 and S4), it is observed that trade liberalization decreases the WF

of consumption globally.

The WF of consumption increases significantly for the regions SSA and MDE in

all scenarios. The biggest change is observed in SSA with an increase by +355% in S1,

+531% in S2, +181% in S3 and +262% in S4. MDE is the region with the second highest

increase: +207% for S1, +294% for S2, +106% for S3 and +146% for S4.

The USA, Canada, ANZ, CAM, SAM, EEU, SAS, SEA and NAF are the other

regions with a higher WF of consumption in 2050 relative to 2000. WEU, JPK, FSU and

CHI have a higher WF of consumption in S1/S2 and a lower in S3/S4 relative to 2000.

Population growth and dietary preferences are the two main drivers of change determining

the future WF of consumption. In many regions of the world, S2 shows the highest WF of

consumption as it has the largest population size with high-meat content diets. S4 shows

higher WF values than S3 due to larger population size in S4 compared to S3.

The largest component of the total WF of consumption is green (67-81% per

scenario), followed by grey (10-20%) and blue (7-13%). Consumption of agricultural

products has the largest share in the WF of consumption, namely 85-93% for all scenarios.

The share of domestic water supply is 2-3% and of industrial products 4-13%.

The WF of consumption of agricultural products is 112%, 180%, 18% and 38%

higher in 2050 than 2000 in S1, S2, S3 and S4, respectively. SSA and MDE show the

highest increase in all scenarios. WEU, JPK, EEU, CHI and FSU demonstrate increases in

WF of consumption in S1/S2 and decreases in S3/S4 compared to 2000. S2 is the scenario

with the highest WF related to consumption of agricultural products in all regions and S3

shows the lowest values among all scenarios.

The main driver of the WF of domestic water supply is population size. The

scenario with the highest population projection, S2, has therefore the highest WF related to

domestic water supply. S3 has the lowest values as it has a relatively low population size

and a reduced WF per household. The regions that show reduction in WF of domestic water


supply in S1, have population sizes lower than 2000. The reductions in S3 are due a

combination of lower estimates of population and reduced per capita domestic water use.

Regarding the WF of consumption of industrial products, all regions show a significant

increase compared to 2000, in all scenarios.

Table 5.6. Percentage change of the WF of consumption relative to 2000. ‘A’ refers to the

WF of agricultural products, ‘D’ refers to the WF domestic water supply, ‘I’ refers to the WF

of industrial products and ‘T’ refers to the total WF.

Region S1 S2 S3 S4

A D I T A D I T A D I T A D I T

USA 29 24 112 41 83 57 69 80 29 -1 50 30 39 12 28 36

Canada 48 26 95 54 91 58 52 83 5 1 55 13 14 13 38 18

WEU 19 -3 112 28 52 22 65 52 -27 -23 52 -19 -24 -13 12 -20

JPK 11 -20 113 19 39 1 50 38 -36 -36 58 -26 -29 -28 15 -25

ANZ 172 40 107 171 201 77 62 199 20 12 73 20 5 26 13 5

EEU 12 -24 1024 143 45 0 285 75 -47 -39 438 17 -41 -30 419 20

FSU 6 -18 975 61 39 10 268 51 -44 -34 366 -20 -37 -24 340 -15

MDE 198 44 720 207 309 88 229 294 99 15 436 106 153 32 152 146

CAM 100 21 865 115 165 63 264 163 9 -3 490 20 24 13 292 30

SAM 117 24 722 126 181 66 204 177 21 -1 370 27 29 15 231 32

SAS 128 38 1206 143 214 85 313 212 27 11 1399 49 55 28 676 64

SEA 96 32 769 117 160 76 169 156 2 6 317 13 16 22 338 27

CHI 79 -12 1391 113 117 16 205 116 -29 -29 346 -18 -25 -19 771 -3

NAF 65 43 811 81 122 90 298 125 25 14 881 45 50 32 171 52

SSA 353 122 1415 355 538 183 334 531 179 78 969 181 263 101 486 262

RoW 212 -9 893 240 274 11 211 259 37 -27 366 52 51 -20 400 67

World 112 18 596 130 180 55 157 175 18 -6 308 30 38 8 259 46

Figure 5.5 shows the contribution of different consumption categories to the total

WF of consumption for 2000 and for different scenarios. Consumption of cereals has the

largest share (26%) in the total WF in 2000. Other products with a large share are meat

(13%), oil crops (12%), poultry (10%), vegetables and fruits (8%) and dairy products (8%).

Meat consumption becomes the major contributor to the WF of consumption in S1 and S2

(19-20%). Oil crops, vegetables, and fruits are the other consumption categories that have a

147

large contribution to the total WF of consumption in S1 and S2. The share of cereals

decreases to 19% in S2 and to 17% in S1. Cereal consumption has the largest share (30%)

in S3 and S4, which are characterized by low meat content diets. Oil crops follow cereals

with 16%. The share of meat consumption decreases in these scenarios to 13%.

Consumption of industrial products becomes another significant contributor in S3 and S4

(7%).

Cereals are the largest contributor to the blue WF of consumption in all scenarios.

Its share is 25% in S1 and S2, and 39% in S3 and S4. Cereals are followed by vegetables

and fruits in S1 and S2 (17%) and by oil crops for S3 and S4 (14%). Other product groups

with a large share in the blue WF of consumption are meat, poultry, dairy products and

sugar crops. The grey WF of consumption is dominated by industrial products and domestic

water supply in all scenarios. The share of industrial products in the grey WF of

consumption increases to 36% in S1 and S2 and 43% in S3 and S4, while it is 28% in 2000.

The WF related to domestic water supply is the second largest contributor to the grey WF

of consumption, with 18% for all scenarios.

Figure 5.5. The contribution of different consumption categories to the total WF of consumption in the world.

14

Figcon

Re

reg

inc

the

tra

SA

the

de

S2

low

1

2

3

4

5

6

48/ Chapter 5. Wa

gure 5.6. The shnsumption (%).

The share

egions with large

gions. The region

crease their depe

e share of the ex

ade is relatively li

AS, SEA and SSA

eir dependencies.

crease their exter

2 and S4, the shar

wer than other tw

0

10

20

30

40

50

60USA

Canada

ater footprint sce

hare of the exter

of the external

e external WFs

ns with a high s

endency on extern

xternal WF in JPK

iberalized compa

A increase their

. The regions with

rnal WF of consu

re of the external

wo scenarios, S1 a

WEU JPK

ANZ

2

narios for 2050

rnal water footpr

WF of consump

apparently depen

share of external

nal water resourc

K will go up to 5

ared to 2000. Our

share of externa

h increased produ

umption. In the sc

l WF of consump

and S3, which are

EEU

FSU

MDE

CAM

2000 S1 S2

rint of consumpt

tion in the total

nd upon freshwa

l footprint in 200

ces in 2050 sign

55% in S1 and t

r scenarios show

al WF while the

uction, like the U

cenarios with incr

ption in the total

e characterized by

CAM

SAM

SAS

SEA

S3 S4

tion in the total

is given in Figur

ater resources in

00 like JPK and

ificantly. For exa

o 56% in S3, in

that WEU, JPK,

other regions de

USA, Canada and

reased self-suffic

WF of consump

y trade liberaliza

SEA

CHI

NAF

SSA

WF of

re 5.6.

n other

MDE

ample,

which

MDE,

ecrease

d ANZ,

ciency,

ption is

ation.

SSA

RoW

149

Figure 5.7. Percentage change of the WF of consumption per capita relative to 2000.

S1

S2

S3

S4


Figure 5.7 shows the change in the WF of consumption per capita per region for

different scenarios relative to 2000 volumes. The world average WF of consumption per

capita increases by +73% in S1, +58% in S2, -2% in S3 and 10% in S4 compared to 2000

volumes. All the regions increase their WF of consumption per capita in S1 and S2

compared to 2000. Canada, WEU, JPK, FSU, CAM, SEA, ANZ, CHI decrease their WF of

consumption per capita in S3 compared to 2000 The other regions have a higher WF of

consumption per capita in S3 than 2000. Most of the regions have lower WFs of

consumption per capita in S4 than 2000 except EEU, MDE and SSA. The regions with

relatively low meat consumption in 2000 experience the biggest change in S1 and S2,

which assume western meat diet patterns in 2050. SSA is a good example for this, where

per capita WF of consumption increases by +92% in S2. The change in the regions with

high meat diet in 2000 already (the USA, Canada and WEU) is relatively lower than other

regions in S1 and S2. A decrease is observed in S3 and S4 in these regions due to reduction

in consumption of animal products except USA in S3. The reason of the increase in per

capita WF of consumption in the USA in S3 is increased biofuel consumption. In the year

2000, the USA has the highest WF per capita in the world. Other regions with a high per

capita WF of consumption are Canada, ANZ, FSU and WEU. In 2050, for the S1 and S2

scenarios, EEU has the highest WF per capita and is followed by the USA, FSU and

Canada. WEU goes down in the ranking and has a lower WF of consumption per capita

than the average of the world in 2050. The regions with higher WF of consumption per

capita than the world average in 2000 also have higher values in S3 and S4, except WEU.

The regions with relatively low WFs will continue to have lower values per capita in all

scenarios (SEA, CHI, and SAS). Among the scenarios, S1 demonstrates the highest WF of

consumption per capita and S4 shows the lowest.

5.4 The water footprint of Europe in 2050

In this section, we examine the WF scenarios for the two European regions (WEU and

EEU) in more detail and zoom in to the country level. We estimate the WF of production

and consumption per nation and per scenario inside Europe. In addition, we address the

virtual water flows between Europe and the other regions of the world and the inter-

national virtual water flows within Europe.

5.4

Th

20

17

20

an

S1

an

in

S4

to

vo

EE

blu

an

86

de

Fig

20

‐

4

4.1 Water footp

he total WF of pr

00 values. It dec

% and 48% larg

00. The blue com

d decreases by 1

, S3 and S4 by 6

The WF o

d falls by 34 and

WEU decreases

4 but increases in

EEU incre

2000, respective

lumes are 36% a

EU shows the big

ue WF of produc

d 81% in S4. Inc

% larger than 20

creases (18-19%

gure 5.8. Percen

000.

100

0

100

200

300

400

500

Green

print of producti

roduction in WEU

creases 36% in S3

ger in S1 and S2

mponent changes

1% in S3 and 1%

, 40, 30% respect

of agricultural pro

d 28% in S3 and

in all scenarios. T

S2 compared to

eases its WF of pr

ely. The increase

and 31% higher th

ggest growth: 448

ction increases si

reases can also b

000 in S1 and S2,

lower than 2000

tage change in t

Blue

WEU

ion

U increases by +

3 and 29% in S4

and 38% and 3

s in a similar way

% in S4 (Figure

tively, and increa

oduction in WEU

S4 compared to

The WF of dome

2000.

roduction by +15

e is lower in S3 a

han in 2000, resp

8% in S1, 174% i

gnificantly as we

be seen in the gree

, respectively. In

0).

the WF of total p

Grey Gree

+12% in S1 and +

. The green WF

2% smaller in S

y: increases by 9

5.8). The grey c

ases by 22% in S2

U increases by 19%

2000. The indus

estic water supply

50% and +107%

and S4 than in th

pectively. The gre

in S2, 197% in S

ell: 231% in S1,

en WF of produc

S3 and S4, the g

production in WE

n Blue

EEU

+42% in S2 rela

of production be

S3 and S4 compa

and 35% in S1 a

omponent decrea

2.

% in S1 and 51%

strial WF of prod

y reduces in S1, S

in S1 and S2 com

he other scenario

ey WF of produc

3 and 179% in S

94% in S2, 93%

ction, which is 51

green WF of prod

EU and EEU rela

Grey

151

ative to

ecomes

ared to

and S2

ases in

% in S2

duction

S3 and

mpared

os, but

tion in

4. The

% in S3

% and

duction

ative to

S1

S2

S3

S4


The WF of industrial production in EEU in S1 becomes 8 times higher than in

2000. The less drastic but still large increase is also detected in the other scenarios. The WF

related to agricultural production becomes larger in S1 and S2, by 50% and 85%,

respectively. It stays below the 2000 volumes in S3 and S4. The WF of domestic water

supply remains on the value of 2000 in S2 and decreases by around 24-39% for S1, S3 and

S4.

Among the agricultural products, the WF related to meat production has the largest

share (28%) in the total for S1 and S2 in WEU. The share of meat production decreases to

19-22% in S3 and S4. Oil crops and cereals increase their share in the total WF of

production in S3 and S4 partly due to the high demand for biofuel by WEU. The WF of

meat production shows the biggest increase in S1 and S2 but it decreases 20% in S3 and S4

compared to 2000. The WF of vegetable and fruit production increases largely in S1 and S2

and decreases by 20%and 30% in S3 and S4 compared to 2000. For most of product

groups, the WF of production increases in S1/S2 and decreases in S3/S4. The total WF of

oil crop and sugar crop production increases in S2 and S4 and decreases in S1 and S3,

compared to 2000.

The WF of agricultural production increases notably in EEU in S1 and S2 for all

product groups. The WFs related to the production of meat, dairy products, vegetables and

fruits multiply more than two times in S1 and S2. However, the total WF of production for

these product groups reduces by 30% in S3 and S4. The total WF of sugar crop and oil crop

production increases in S1, S2 and S4 compared to 2000. The increase in the overall WF of

agricultural production is the highest in S2 because of the large population size and high

meat content diet in this scenario.

On national level, Eastern European countries like Poland, Hungary, Bulgaria and

Romania become important producers and significantly increase their WF of consumption

in S1 and S2 compared to 2000 (Figure 5.9). The countries with the highest WF of

production in 2000, like France and Spain, continue to have the largest WF of production in

2050. A shift from Southern Europe to Northern Europe is observed in the WF of cereal

153

production. Norway, Luxembourg, Iceland, Cyprus and Malta have the highest increase of

WF of production in S1 and S2 compared to 2000.

Figure 5.9. Percentage change of the WF of total production relative to 2000.

All Eastern European countries have a higher WF of production in S3 and S4

relative to 2000 although the increase (around 30%) is smaller than the increases in the WF

of production observed in S1 and S2. All of the WEU countries decrease their WF of

production in S3 compared to 2000, except Cyprus, Malta, Iceland and Norway. A

reduction in WF of production is seen in the Netherlands, Belgium, Sweden, Germany, the

UK, Ireland, Austria, Switzerland and Denmark in S4 compared to 2000. Spain and Italy,

two counties with a large WF of production in 2000 in Europe, decrease their WF of

production relative to 2000 only in S3 among all scenarios. Low-meat content diets and a

shift of production to Central and Eastern Europe are the main reasons for this. Among the

WEU countries, the Netherlands and Denmark have the highest reduction in the total WF of

production compared to 2000, in S3 and S4. France reduces its WF of production in S3 but

S1 S3

S2 S4


increases in S4 compared to 2000. Germany has a lower WF of production in S3 and S4

compared to 2000.

Figure 5.10. Percentage change of the blue WF of production relative to 2000.

The small island countries in Europe, Cyprus and Malta, increase their blue WF of

production in S1 and S2 significantly (two and six times higher than 2000). These countries

already experience high blue water scarcity so scenarios S1 and S2 will be very problematic

for these countries. The blue WF of production in Malta increases significantly in S3 and

S4 as well. Spain, another country with large water scarcity, decreases its blue WF of

production by 3% in S1, 27% in S3, 5% in S4 but it increases its blue WF of production by

32% in S2. Italy, Portugal, Denmark, France, Ireland, the Netherlands and the UK have

higher blue WFs of production in S1 and S2 than 2000 and lower blue WFs of production

in S3 and S4 than 2000. Austria, Finland, Norway, Iceland, Sweden, Belgium, Switzerland

and Luxembourg increase their blue WF of production in all scenarios (Figure 5.10).

Most of the EEU countries double their blue WF of production in S1 and S2. They

also have higher blue WF of production in S3 and S4, except Croatia and Bosnia and

S1 S3

S2 S4

155

Herzegovina. Serbia, the Czech Republic, Slovenia and Macedonia have the highest

increase in blue WF of production in EEU.

5.4.2 Virtual water flows between countries

WEU is a net virtual water importer in 2000 (Figure 5.11). It remains a net virtual water

importer; however, it decreases its net virtual water import in S2 and S4 compared to 2000.

It increases its net virtual water import by +2% in S1 and +10% in S3. The reduction in net

virtual water import by WEU is -35% in S2 and -40% in S4. The net virtual water imports

to WEU were mainly from SEA, SAM, FSU, CHI and SSA in 2000. The virtual water

import from SAM increases by around +200% in S1, S2 and S3 and +120% in S4, which

makes SAM the biggest virtual water exporter to WEU in 2050. Although SEA has a large

net virtual water export to WEU in 2050, its net virtual water export to WEU decreases by -

35% for S1, S2 and S3 and -55% for S4. The net virtual water imports from Canada, EEU

and ANZ decrease as well, more than -50% in all scenarios. The net virtual water import

from the USA increases more than 10 times in 2050 for all scenarios but remain relatively

small compared to the net virtual water exports from other regions. The virtual water import

volume from FSU increases by 210% in S1, 100% in S2 and S3 but decreases by 4% in S4.

WEU increases its net virtual water import from China by +410% in S1 and more than

+100% in S2, S3 and S4. Being net virtual water exporters to WEU in 2000, SSA and MDE

become net virtual water importers from WEU in 2050 for all scenarios. WEU is a net

virtual water exporter to SAS, MDE, NAF, SSA and JPK in 2050. The largest net virtual

water export is to SSA in all scenarios, followed by SAS and MDE. The net virtual water

exports by WEU to SSA increases significantly in 2050 due to increased trade of animal

products.

EEU, a net virtual water exporter in 2000, remains a net virtual water exporter in

2050. It considerably increases its net virtual water export, by +100% S4 up to +500% in

S2 compared to 2000 (Figure 5.12). Its virtual water exports are higher than its imports

from all the regions except the USA, Canada, CHI, SAM, FSU, CAM and ANZ in 2050.

The largest net virtual water flow from EEU is to SSA, MDE and SAS in 2050. Being a net

virtual water exporter to CHI and FSU in 2000, EEU becomes a net virtual water importer

15

fro

hig

Fig

Fig

gre

56/ Chapter 5. Wa

om these regions

ghest in S1 and lo

gure 5.11. Net vir

gure 5.12. Net vir

Figure 5.1

een, blue and gr

‐150 ‐100

USA C

SAM S

‐100.00 ‐80.00

USA C

SAM S

ater footprint sce

in 2050. Among

owest in S3.

rtual water import

rtual water import

13 shows the ne

rey components.

‐50 0

2000

S1

S2

S3

S4

Canada JPK

SAS SEA

‐60.00 ‐40.00

Canada WEU

SAS SEA

narios for 2050

g the scenarios, n

t by WEU specifie

t by EEU specifie

t virtual water f

WEU is a net b

0 50

ANZ EEU

CHI NA

0 ‐20.00 0.0

2000

S1

S2

S3

S4

JPK AN

CHI NA

net virtual water

ed by region (Gm

d by region (Gm3

flows from/to W

blue virtual wate

100 150

U FSU

F SSA

0 20.00 40

Z FSU

F SSA

r import by EEU

3/year).

3/year).

WEU and EEU by

er importer in 20

0 200

MDE CAM

RoW

.00 60.00 8

MDE CAM

RoW

U is the

y their

000. In

250

0.00

157

2050, WEM becomes a net blue virtual water exporter in S2 and S4. By 2050, most of the

net blue virtual water flows from WEU are to SSA, SAS and MDE and net blue virtual

water imports to WEU are from SAM, the USA and ANZ. From the green water

perspective, WEU is a net virtual water importer in all scenarios. As for grey component,

WEU continues to be a net importer in 2050 and increases its net virtual water import by

+143% in S1, +29% in S2, +74% in S3. EEU is a net virtual water exporter in terms of

green and blue components in 2050. It is a net grey virtual water importer in S1, S3 and S4

and exporter in S2. The green component has the biggest share in net virtual water exports

from EEU.

The net virtual water import to WEU is mainly related to crop products and

industrial products. The region is a net virtual water exporter considering animal products

in 2050 (Figure 5.14). The net virtual water export related to animal products increases very

substantially in EEU as well. Although EEU is a net virtual water exporter in 2000

regarding all product groups, it becomes a net virtual water importer related to industrial

products in 2050.

The virtual water export from EEU to WEU is larger than imports, therefore a net

virtual water flow from EEU to WEU is observed in 2000. This continues towards 2050 but

the net virtual water import by WEU from EEU is reduced largely in S1, by -90%.

Figure 5.13. Net virtual water import by WEU and EEU specified by green, blue and grey

components (Gm3/year).

‐20

0

20

40

60

80

100

120

2000 S1 S2 S3 S4

Grey

Blue

Green

‐80

‐60

‐40

‐20

0

20

40

60

2000 S1 S2 S3 S4

WEU EEU


Figure 5.14. Net virtual water import by WEU and EEU specified by commodity group

(Gm3year).

Figure 5.15 shows net virtual water imports per nation in Europe for 2000 and four

scenarios. All WEU countries are net virtual water importers in 2000. Countries like

France, Spain, Ireland, Denmark, Greece and the Netherlands become net virtual water

exporters for scenarios S1 and S2. In particular, the change in France is quite big. The UK,

Italy, Portugal, Sweden, Norway, Finland, Germany, Austria, Belgium, Switzerland, Malta,

Cyprus and Iceland remain net virtual water importers in S1 and S2. The net virtual water

flow changes direction for some countries in S3. Spain and the Netherlands are net

importers in S3. France, Denmark, Greece, and Ireland are net virtual water exporters in S3

and S4.

Romania, Bulgaria, Serbia and Montenegro are net virtual water exporters in 2000

and stay so in 2050. Poland, the Czech Republic and Hungary are net virtual water

importers in 2000 and become net virtual water exporters in 2050. Slovakia, Macedonia,

Bosnia and Herzegovina, Croatia and Albania are net virtual water importers in 2000 and

2050. Slovenia is a net virtual water exporter in S1 and S2 and a net virtual water importer

in S3 and S4.

‐80

‐60

‐40

‐20

0

20

40

60

2000 S1 S2 S3 S4

Crop products Animal products

Industrial products

‐200‐150‐100‐50050

100150200250

2000 S1 S2 S3 S4

Crop products Animal products

Industrial productsWEU EEU

159

1.1

Figure 5.15. Net virtual water import per European country (Gm3year).

< -20

-20 - -10

-10 - -5

-5 - 0

0 - 5

5 - 10

10 - 20

>20

2000

S1 S3

S2 S4

16

5.4

Th

to

inc

in

by

hig

57

in

wi

tot

of

gre

sha

Fig

Me

60/ Chapter 5. Wa

4.3 Water footp

he total WF of co

2000. The WF o

creases its WF of

S2, +17% in S3

y +30% in S1 and

gher WF of cons

% in S2, 38% in

WEU is green, i

ith the share of 2

tal in EEU decrea

consumption in E

een, blue and gre

ares in 2000.

gure 5.16. The co

The WF o

eat and cereals ar

ater footprint sce

print of consump

onsumption in W

of consumption in

f consumption in

and +20% in S4

d +22% in S2 an

sumption per cap

S3 and 23% in S

in both 2000 and

20% and 10%, re

ases from 73% in

EEU increases fr

ey components in

omposition of the

of consumption p

re the product gro

narios for 2050

ption

WEU increases by

n WEU decrease

all scenarios com

4. The WF of con

nd decreases by -

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161

2000. The share of meat consumption decreases in S1 and S2. It falls down considerably in

S3 and S4 scenarios. The WF related to the consumption of industrial products doubles its

share in 2050 compared to 2000. Other commodities with a large share in total WF of

consumption in 2050 are cereals and oil crops. Especially the share of oil crops

significantly increases in S3 and S4, due to drastic biofuel expansion.

The blue WF of consumption in Europe is mainly due to industrial products in

2050 (Figure 5.17). Vegetables and fruits are the second biggest contributor to the total blue

WF of consumption in 2050 (14-16%). The share of oil crops in total blue WF of

consumption increases with 9% in S1, 12% in S2, 14% in S3 and 20% S4. The share of

blue WF of meat consumption in total blue WF of consumption is 12% in S1 and S2, 8% in

S3 and 7% in S4. Other product groups with large share in total blue WF of consumption

are dairy products, domestic water supply and cereals in all scenarios.

Figure 5.17. The composition of the blue WF of European consumption by commodity.


Figure 5.18. The composition of the grey WF of European consumption by commodity.

The grey WF of consumption is mainly from industrial products, with the share of

66% in S1 and S2 and 69% in S3 and S4. Domestic water supply is another big contributor

to the total grey WF of consumption, 7% of the total grey WF of consumption in all

scenarios. Other product groups with a large share in total grey WF of consumption are

dairy products (6-7%), cereal (5-6%), meat (4-7%), vegetables and fruits (2-3%). The

composition of the grey WF of consumption does not differ much from scenario to scenario

(Figure 5.18).

The change in WF of consumption per capita relative to 2000 for the nations of

Europe is shown in Figure 5.19. All WEU countries have a higher WF of consumption per

capita in S1 and S2 than 2000, except Denmark, Ireland, Luxembourg and the Netherlands.

Belgium, Sweden, Cyprus, Iceland and Malta have higher WF of consumption per capita in

2050 than 2000. Austria, France, Greece, Italy, Norway, Portugal, Spain, Switzerland and

the UK decrease their WF of consumption in S3 and S4 compared to 2000. Italy, the

Netherlands, Spain, Switzerland, Luxembourg and the UK reduce their WF of consumption

per capita values by more than -20% in S4. Among WEU nations, Cyprus, Malta and

Iceland significantly increase their WF of consumption per capita in S1 and S2. Spain has

the highest WF of consumption per capita in 2000. In 2050, Malta has the highest WF of

consumption per capita.

163

Figure 5.19. Percentage change of the WF of consumption per capita relative to 2000.

In 2050, EEU countries have a higher WF of consumption per capita than in 2000.

Bulgaria, Hungary, Croatia, Macedonia and Bosnia and Herzegovina increase their WF

consumption per capita by more than +100% in S1 and S2. Montenegro is the only country

in EEU with a reduction in WF of consumption per capita in S2.

The share of external WF of consumption in total WF of consumption increases in

WEU in 2050. However, these countries with a very high external WF consumption share

in 2000 like the Netherlands (94%), Malta (90%) and Belgium (90%) significantly reduce

this ratio below 50% in all scenarios. The UK, Switzerland and Luxembourg have an

external WF of consumption share more than 60% in all scenarios. Spain significantly

reduces its share of external WF of consumption in 2050. All of the EEU counties reduce

the share of external WF of consumption in S2, S3 and S4.

S2

S3

S4

S1


5.5 Discussion and conclusion

This study is the first global water footprint scenario study. It explores how the water

footprint of humanity will change towards 2050 under four alternative scenarios, which

differ from each other in terms of specific trajectories for the main drivers of change.

Although we included the major drivers of change in our analysis, some of them were kept

outside the scope of this study. First, we excluded the impact of resource availability. The

constraints related to water and land availability are only addressed implicitly in the

production and trade scenarios. A future step would be to integrate such limitations

explicitly. Climate change effects are partially addressed in our study. We implicitly

included the impact of climate change on production and trade patterns, but we excluded

CO2 fertilization effects in yields and climate change effects on crop water use. Another

limitation is that we assumed a homogeneous and single industrial sector in estimating the

water footprint of industrial production and consumption.

Our analysis shows that water footprints can radically change from one scenario to

another and are very sensitive to the drivers of change:

• Population growth: The size of the population is the major driver of change of the WF

of production and consumption. The WF of production and consumption is the largest

in the scenario in which the population projection is the highest (S2).

• Economic growth: The effect of economic growth is observed in terms of income

levels and GDP changes. Increased income levels result in a shift toward high

consumption of water intensive commodities. GDP growth significantly increases

industrial water consumption and pollution. S1 has the highest WF of industrial

production and consumption among all scenarios because it foresees the highest GDP.

• Consumer preferences: The diet of people strongly influences the water footprints of

consumption and production. Diets with increased meat and dairy products result in

very high water footprints in 2050 (S1 and S2 scenarios). In S3 and S4, the scenarios

with low meat content, the total water footprint of consumption and production in the

165

world drastically decrease. This shows us that a reduction in humanity’s water

footprint is possible in 2050 despite population increase.

• Biofuel use: Existing plans related to biofuel use in the future will increase the

pressure on water resources. The study shows that a high demand of biofuel increases

the water footprint of production and consumption in the world and especially in

Western Europe, the USA and Brazil.

• Importance of international trade: A reduction in water footprints is possible in 2050

by liberalization of trade (S1 versus S2 and S3 versus S4). Trade liberalization, on the

other hand, will imply more dependency of importing nations on the freshwater

resources in the exporting nations and probably energy use will increase because of

long-distance transport.

• Climate change: Global agriculture production and trade structure will be affected by

climate change. The production volumes will decrease in some parts of the world and

will increase in others. The production changes across the world will affect the water

footprint of production. In overall, our results show total water footprint of production

of humanity will decrease because of climate change effects on global agricultural

production. However, it does not result in similar change in all parts of the world. It

will increase the water footprint of production in Europe, Australia and East Asia

decrease the water footprint of production in the USA, Middle East and Russia.

Evidently, climate change will also affect water availability and scarcity around the

world differently and this should be combined with this information carefully.

• Technology: Technologic development directly affects water productivity, water use

efficiency and wastewater treatment levels. Increased water productivity as a result of

technological development result in reduction of the water footprint of consumption

and production.

From the European point of view, this study shows that the most critical driver of

change that affects future WF of production and consumption for Europe is consumption

patterns. The WF of production and consumption in WEU increase in the ‘high-meat’


scenarios (S1 and S2) and decrease in the ‘low-meat’ scenarios (S2 and S3). In addition,

extra demand created by biofuel needs put extra pressure on European water resources (S3

and S4). The European countries with a high external WF of consumption ratio in 2000

decrease their dependencies on foreign water resources (e.g. the Netherlands, Belgium and

Luxembourg).

This study shows how different driver will change the level of water consumption

and pollution globally in 2050. These estimates can form an important basis for a further

assessment of how humanity can mitigate future freshwater scarcity. We showed with this

study that reducing humanity’s water footprint to sustainable levels is possible even with

increasing populations, provided that consumption patterns change. This study can help to

guide corrective policies at both national and international levels, and to set priorities for

the years ahead in order to achieve sustainable and equitable use of the world’s fresh water

resources.

167

Appendix 5.1: Countries and regional classification

Region code Region Countries

1 USA United States of America

2 Canada Canada

3 WEU Andorra, Austria, Cyprus, Denmark, Finland, France, Germany Greece, Iceland, Ireland, Italy, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, Belgium, Luxembourg

4 JPK Japan, Dem. People's Republic of Korea

5 ANZ Australia, New Zealand

6 EEU Albania, Bulgaria, Bosnia and Herzegovina, Hungary, Croatia, TFYR Macedonia, Czech Republic, Poland, Romania, Slovenia, Slovakia, Serbia Montenegro

7 FSU Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan

8 MDE Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Qatar, Saudi Arabia, Syrian Arab Republic, Oman, Turkey, United Arab Emirates, Yemen, Occupied Palestinian Territory

9 CAM Caribbean, El Salvador, Grenada, ,Mexico, , Nicaragua, Panama

10 SAM Argentina, Bolivia , Brazil, Chile, Colombia, Ecuador, ,French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela

11 SAS Afghanistan, Bangladesh, Bhutan, Sri Lanka, India, Maldives, Nepal, Pakistan

12 SEA Brunei, Darussalam, Myanmar, Indonesia, Cambodia, Laos, Malaysia, Philippines, Timor-Leste, Singapore, Thailand, Viet Nam

13 CHI China

14 NAF Algeria, Egypt, Libyan Arab Jamahiriya, Morocco, Western Sahara, Tunisia

15 SSA Angola, Botswana, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Benin, Equatorial Guinea, Djibouti, Gabon, Gambia, Ghana, Guinea, Côte d'Ivoire, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Niger, Nigeria, Guinea-Bissau, Eritrea, Zimbabwe, Réunion, Rwanda, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania Togo, Uganda, Burkina Faso, Ethiopia, Congo, Zambia, Mayotte

16 RoW Other non-specified areas (Rest of the World)


Appendix 5.2: Population and GDP forecasts

Population

Region Region S1-2050 S2-2050 S3-2050 S4-2050 1 USA 357,007,000 452,394,000 357,007,000 403,100,000 2 CAN 38,845,000 48,791,000 38,845,000 43,641,000 3 WEU 385,569,000 487,475,000 385,569,000 434,634,000 4 JPK 119,338,000 151,811,000 119,338,000 134,930,000 5 ANZ 32,903,000 41,515,000 32,903,000 37,063,000 6 EEU 93,422,000 122,034,000 93,422,000 107,097,000 7 FSU 239,902,000 320,767,000 239,902,000 278,366,000 8 MDE 403,048,000 525,568,000 403,048,000 461,667,000 9 CAM 225,896,000 304,142,000 225,896,000 262,882,000 10 SAM 419,973,000 564,683,000 419,973,000 488,073,000 11 SAS 1,990,834,000 2,660,586,000 1,990,834,000 2,308,540,000 12 SEA 655,577,000 872,810,000 655,577,000 759,206,000 13 CHI 1,130,211,000 1,479,309,000 1,130,211,000 1,295,603,000 14 NAF 200,112,000 265,577,000 200,112,000 231,496,000 15 SSA 1,731,742,000 2,204,177,000 1,731,742,000 1,960,102,000 16 RoW 81,243,000 98,602,000 81,243,000 89,589,000 17 World 8,105,622,000 10,600,241,000 8,105,622,000 9,295,989,000

GDP (1990 US$ MEX)

Region d

Region S1-2050 S2-2050 S3-2050 S4-2050 1 USA 21758785042065 17355484996242 19249661687548 16414353161340 2 CAN 1853763940822 1446163121773 1836185439521 1634556518207 3 WEU 23553103572664 18374308098248 21125539816549 15553707375045 4 JPK 9082835848045 6430169278057 8424823206354 6141977795023 5 ANZ 948923321297 740344752629 988793402357 643490166991 6 EEU 3366254712014 1153188770022 2015141767763 1941334293422 7 FSU 10005897982675 3427752851389 5416984803691 5115675629960 8 MDE 12575743797432 5038190390666 10267041500486 4821689528756 9 CAM 7091959678872 2674606474631 5426717678644 3598373232136 10 SAM 15866432073256 5873001540866 11334363400416 7985386183870 11 SAS 12836624204768 4063470017500 18425511851559 9533839146457 12 SEA 10838071266980 3354806708623 6509907246729 6832038421202 13 CHI 25718590039554 5262355190158 9620976808823 18778528893685 14 NAF 4954364041239 2164826449829 6669075096881 1844179845178 15 SSA 13162859514781 3768938467049 11610784630915 6362618805968 16 RoW 8253990018825 2585651189853 4844943399150 5195770140139

169

Appendix 5.3: Coefficient for change in unit water footprint of agricultural commodities per region per scenario (α values)

Region

S1 & S2 S3 & S4

Blue WF Green WF Grey WF Blue WF Green WF Grey WF

USA 0.80 1.00 1.00 1.00 0.80 0.80

Canada 0.80 1.00 1.00 1.00 0.80 0.80

WEU 0.80 1.00 1.00 1.00 0.80 0.80

JPK 0.80 1.00 1.00 1.00 0.80 0.80

ANZ 0.80 1.00 1.00 1.00 0.80 0.80

EEU 0.80 1.00 1.00 1.00 0.70 0.70

FSU 0.80 1.00 1.00 1.00 0.70 0.70

MDE 0.80 1.00 1.00 1.00 0.80 0.80

CAM 0.70 1.00 1.00 1.00 0.80 0.80

SAM 0.70 1.00 1.00 1.00 0.80 0.80

SAS 0.70 1.00 1.00 1.00 0.60 0.60

SEA 0.70 1.00 1.00 1.00 0.60 0.60

CHI 0.70 1.00 1.00 1.00 0.60 0.60

NAF 0.80 1.00 1.00 1.00 0.80 0.80

SSA 0.60 1.00 1.00 1.00 0.80 0.80

RoW 0.80 1.00 1.00 1.00 0.80 0.80


Appendix 5.4: Agricultural production changes in 2050 relative to the baseline in 2050

Region

Baseline relative to 2000 (%)

Percentage change relative to baseline 2050

TL1 TL2 A1B A2 A1B+T1 A1B+TL2 A2+TL1 A2+TL2

USA 89 -0.41 -0.35 -9.20 -10.12 -9.40 -9.36 -10.31 -10.28

CAN 50 0.66 1.76 -10.04 -8.53 -9.53 -8.78 -7.99 -7.21

WEU 5 0.21 -0.41 4.30 4.83 4.73 4.19 5.27 4.72

JPK 6 -0.26 0.22 6.47 6.86 6.61 7.52 7.01 7.85

ANZ 67 1.48 1.49 6.95 9.49 8.40 8.41 10.90 10.93

EEU 33 -0.14 -0.24 2.59 2.29 2.49 2.43 2.18 2.12

FSU 79 -0.15 -0.18 -21.28 -20.42 -21.30 -21.28 -20.41 -20.39

MDE 93 0.11 0.08 -23.24 -16.81 -23.23 -23.22 -16.76 -16.75

CAM 139 -0.12 -0.19 -1.70 -2.70 -1.81 -1.89 -2.80 -2.88

SAM 248 0.21 0.16 -1.77 -1.81 -1.65 -1.76 -1.70 -1.80

SAS 84 -0.73 -0.76 -3.16 -2.17 -3.89 -3.84 -2.97 -2.93

SEA 101 0.01 0.04 -11.63 -12.28 -11.74 -11.68 -12.40 -12.34

CHI 31 0.20 0.37 11.18 9.04 11.54 11.88 9.36 9.68

NAF 110 0.12 -0.17 -8.90 -13.73 -8.91 -9.00 -13.73 -13.81

SSA 158 -0.29 -0.39 3.54 3.69 3.24 3.13 3.39 3.28

RoW 173 0.91 0.93 -3.58 -3.64 -2.82 -2.79 -2.89 -2.86

Source: Calzadilla et al. (2011a)

6 Understanding carbon and water footprints: similarities and contrasts in concept, method and policy response5

Abstract

The objective of this study is to analyse the origins and the characteristics of the carbon and

water footprints in order to understand their similarities and differences and to derive

lessons on how society and business can adequately build on the two concepts. We compare

the two concepts from a methodological point of view and discuss response mechanisms

that have been developed, with the hope that experiences in one field might be able to

benefit the other.

The carbon and water footprint concepts were introduced about a decade ago,

simultaneously, but independently from one another. The ‘carbon footprint’ concept has

become popular over the past few years – since, more or less, 2005 – and is currently

widely accepted and used by the public and media despite its lack of scientifically accepted

and universally adopted guidelines: it describes greenhouse gas emission measurement

from the narrowest to the widest sense. Several calculation methods and approaches for

carbon footprint accounting have been proposed and are being used. Since about 2008,

‘water footprint’ has also become a popular term. Although the meaning and methodology

of the water footprint were well defined in the scientific literature in the early stages of its

inception, there is still an immense potential for less rigorous usage of the term, similar to

the fate of the carbon footprint. The ambiguity around the concept of the carbon footprint

could become a problem for the water footprint concept in the near future. By drawing

lessons from the history and progress of the carbon footprint and understanding the

development and mechanisms of carbon footprint assessment (both accounting and

response formulation), we can draw lessons that may help reduce the risk that the water

footprint will lose its strict definition, interpretation and usage.

In response to the increasing concern about climate change and global warming,

governments, businesses and consumers are considering ways to reduce greenhouse gas

5 Based on Ercin and Hoekstra (2012b)

172/ Chapter 6. Understanding carbon and water footprints

emissions. The two main response strategies are reduction and offsetting. Reduction refers

to undertaking activities in a less carbon intensive way; offsetting refers to taking external

actions to compensate for carbon footprints by means of some form of carbon capture or

reduction elsewhere (by others). These strategies are applied and supported widely by

business and government. However, two issues seriously challenge the effective reduction

of humanity’s carbon footprint. The first is the absence of a unique definition of the carbon

footprint, making reduction targets and statements about carbon neutrality difficult to

interpret, and leaving potential for developments to look better than they really are. The

second problem is that existing mechanisms for offsetting leave room for creating

externalities and rebound effects. In the case of the water footprint, the question of how to

respond is still under debate, but it has been recognized that reduction and offsetting

strategies can be distinguished here too. The terms ‘water neutral’ and ‘offsetting’ have

been considered. The strategy of water offsetting may face the same problem as in carbon

offsetting, but there is an additional problem: water footprints impact at specific locations

and in specific periods of time, and offsetting can only be effective if the offsetting efforts

relate to them.

Carbon footprint accounting has been promoted by companies, non-governmental

organizations and private initiatives and has not primarily been driven by research. This

situation has led to the concept having many definitions, methods of calculation and

response formulations. Some companies are responding rapidly to formulate schemes to

tout their carbon neutrality, but the response is often driven by the interest in brand and

image – many businesses see benefits in using the carbon footprint as a marketing tool

rather than as a tool to measure their contribution to climate change. Carbon accounting,

labelling and meeting the requirements of reduction or offsetting schemes tend to become

goals in themselves rather than supportive instruments to effectively mitigate climate

change. Carbon offsets distract attention from the wider, systemic changes and collective

political action required to tackle climate change. These insights can be helpful in the

search for effective instruments that can contribute to a more efficient, sustainable and

equitable use of the globe’s water resources.

173

Global warming and reduction of greenhouse gas emissions are at the top of the

environmental policy agenda today. However, the way in which the concept of the carbon

footprint has been embraced and interpreted in all possible directions and the fact that

reduction schemes are often ill-defined creates unnecessary additional challenges in

effectively tackling environmental problems. We argue in this study that the weakness of

offsetting in the case of the carbon footprint shows that applying both offsetting and

neutrality in the water footprint cannot be effective. A more effective tool may well be

direct water footprint reduction targets to be adopted by both government and business.

6.1 Introduction

The Earth’s climate is changing as a result of anthropogenic activity since the start of the

industrial revolution. There is growing scientific evidence that burning fossil fuels

contributes to rising temperatures and extreme weather events (Mitchell et al., 2006;

Rosenzweig et al., 2001; Solomon et al., 2007). The public and decision-makers have

started to recognize the need for action to mitigate global warming (Goodall, 2007).

Governments, policy-makers and businesses have been urged to seek ways to reduce

greenhouse gas (GHG) emissions in response to growing interest and concern about climate

change over the past two decades (Bo et al., 2008; Brenton et al., 2009; Courchene and

Allan, 2008; Matthews et al., 2008). This brings the need to understand what activities

drive GHG emissions and how they can be effectively reduced. The ‘carbon footprint’ (CF)

concept has become a popular tool to estimate GHG emissions related to human activities

(Moss et al., 2008; Wiedmann, 2009a; Wiedmann and Minx, 2007).

Climate change has received a lot of attention at international forums among

politicians and business leaders in the past decade. Freshwater scarcity has recently become

an important subject on the environmental agendas of governments and companies as well.

Across the media, decision-makers and the public, there is much talk of a looming ‘water

crisis’, which would have impacts on all sectors of the economy, but would primarily affect

food security. Freshwater in sufficient quantity and of adequate quality is not only a

prerequisite for human societies but also for natural ecosystems (Costanza and Daly, 1992).

The unsustainable use of freshwater resources by humans is manifested all around the


world in aquifers gradually becoming depleted, rivers running dry, and water quality

deteriorating (Postel, 2000). Overexploitation of water resources for human activities

affects societies but also jeopardizes the health of ecosystems. Therefore, there is a growing

demand for new approaches and indicators in the field of water resources management that

can help find the main drivers of unsustainability and identify solutions towards sustainable

water use, satisfying increased demand for food, domestic water supply, and goods and

services, but protecting vital ecosystems.

Understanding the consequences of human appropriation of freshwater resources

requires an analysis of how much water is needed for human use versus how much is

available, where and when (Hoekstra and Chapagain, 2008; Lopez-Gunn and Llamas,

2008). Uncovering the link between consumption and water use is vital to formulate better

water governance. The term ‘water footprint’ (WF) was primarily formulated in the

research context, to study the hidden links between human consumption and water use and

between global trade and water resources management (Hoekstra, 2003). The concept helps

us understand the relationships between production, consumption and trade patterns and

water use and the global dimension in good water governance (Hoekstra, 2011).

The WF and CF concepts have similarities; however, their roots and intended

purposes differ. The CF was formulated to quantify the contribution of various activities to

climate change. The history of the WF lies in the exploration of water use along supply

chains and in the search for a tool to understand the global dimension of water as a natural

resource. Although each footprint has different roots and characteristics and addresses

different research and policy questions, there is a tendency among practitioners in the fields

of environmental policy and corporate social responsibility to treat the WF in a similar way

as the CF. For example, popular terms such as ‘carbon neutral’ and ‘carbon offsetting’ are

immediately adapted to ‘water neutral’ and ‘water offsetting’ without any particular

attention to the appropriateness and applicability of these ideas for water. Similarly,

initiatives are taken to develop water labels for products in analogy to carbon labels and to

incorporate the WF into Life Cycle Assessment (LCA) for products in the same way as was

done with the CF. Most notably, people have a tendency to interpret the numbers of the WF

without considering their spatial and temporal characteristics as is commonly done in CF

175

analysis. Each footprint needs to be seen within its appropriate context and interpreted with

care as it is built around different research questions and tells a different story.

The objective of this study is to analyse the origins and the characteristics of the

carbon and water footprints in order to understand their similarities and differences and to

derive lessons on how society and business can adequately build on the two concepts. We

compare the two concepts from a methodological point of view and discuss response

mechanisms that have been developed, with the hope that experiences in one field might be

able to benefit the other.

6.2 Origins of the carbon and water footprint concepts

The carbon and water footprint concepts were introduced about a decade ago,

simultaneously, but independently from one another. The CF arose out of the debate on

climate change, as a tool to measure GHG emissions. The WF was introduced in the field

of water resources management, as a tool to measure water use in relation to consumption

patterns. In both cases, the terminology chosen was inspired by the ecological footprint

(EF), which had been introduced in the 1990s (Rees, 1992). All footprints measure, in

different ways, human appropriation of the planet’s natural resources and carrying capacity

(Galli et al., 2012; Giljum et al., 2011; Hoekstra, 2009) (Figure 6.1). The EF measures the

use of bioproductive space in hectares; the WF measures the consumption and

contamination of freshwater resources in cubic metres per year; and the CF measures the

emission of gases that contribute to heating the planet in carbon dioxide (CO2)-equivalents

per unit of time or product. A common property of all footprints is that they can be related

to specific activities, products and consumption patterns. Recently, the nitrogen footprint

was introduced as a tool to measure the amount of nitrogen released into the environment in

relation to consumption (Leach et al., 2012). In this report, we focus on the CF and WF.


Figure 6.1. Footprint concepts

6.2.1 The carbon footprint

Concern about climate change started with the scientific recognition of the relationship

between CO2 emissions and global warming. The increasing worldwide interest in the

causes and consequences of climate change, and in exploring ways to respond, resulted in

the formation of the Intergovernmental Panel on Climate Change (IPCC) in 1988. The

IPCC was the first worldwide effort to create awareness of global warming and to feed

scientific insights on climate change to governments. The IPCC released its first assessment

report in 1990 (Houghton et al., 1990). This report played an important role in the

establishment of the United Nations Framework Convention on Climate Change

(UNFCCC), an international environmental treaty with the goal of stabilizing GHG

concentrations in the atmosphere at a level that prevents dangerous anthropogenic

interference with the climate system. Efforts under the UNFCCC led to the Kyoto Protocol

(UN, 1998), an international agreement to cut GHG emissions, with specific reduction

targets by country, signed in December 1997 and entered into force in 2005. The overall

goal was a collective reduction of GHG emissions by 5.2% in 2012 compared to the

emission levels of 1990.

Carbon FootprintMeasures the emission of gases that contribute

to global warming

Water FootprintMeasures the consumption and contamination

of freshwater resources

Ecological FootprintMeasures the use of bio‐productive space

Nitrogen FootprintMeasures the amount of nitrogen released into

the environment in relation to consumption

Activities, products and consumption patterns that affect Earth's natural resources and

carrying capacity

177

To achieve its goal, the Kyoto Protocol installed a system for emissions trading

and some mechanisms to allow for offsetting GHG emissions. The system of emissions

trading (the ‘carbon market’) allows countries to sell unused emission permits to countries

that are over their targets. In addition to trade in emission permits (so-called assigned

amount units [AAUs]), the Kyoto Protocol also allows trade in credits that can be obtained

through various offsetting mechanisms:

1. Clean Development Mechanism (CDM): an industrialized country with an

emission-reduction or emission-limitation commitment can implement emission-

reduction projects in developing countries. In this way, the country earns saleable

certified emission reduction credits (CERs).

2. Joint Implementation (JI): an industrialized or in-transition country with an

emission-reduction or emission-limitation commitment can earn emission

reduction units (ERUs) from an emission-reduction or emission-removal project in

another industrialized country or a country in transition.

3. A mechanism that allows countries to earn removal units (RMUs) through projects

that sequester CO2, such as reforestation.

CERs, ERUs and RMUs are all expressed in CO2 equivalents and can all be traded

on the carbon market and counted by a country towards meeting its Kyoto target. Parallel to

the formal carbon market under the Kyoto Protocol, in which companies, governments and

other entities buy emission rights or carbon offsets to comply with caps on the total amount

of CO2 they are allowed to emit, another, voluntary carbon market has grown, in which

individuals, companies and governments purchase carbon offsets to voluntarily mitigate

their GHG emissions. The CF is increasingly used as the stick by which to measure the

volume of GHG emissions related to specific activities or products.

The CF can be seen as an offspring of the EF concept, which was developed by

Wackernagel and Rees (1996). The EF, expressed in hectares, includes a component that

represents the area required to sequester enough carbon emissions to avoid an increase in

atmospheric CO2 (Wackernagel et al., 2002). In this sense, the EF ‘includes’ a carbon


footprint (expressed in hectares). However, the focus on land requirement in the EF is not

very helpful if the interest is not so much in land requirement but more directly in the

volume of CO2 and other GHG emissions. Thus, in response to the interest of governments

and companies in GHG emissions and global warming, the CF has become a modified,

independent concept, expressed in terms of emitted CO2 equivalents (East, 2008; Moss et

al., 2008). It is not clear when and by whom the term CF was used for the first time, but it is

found in newspaper articles as early as the year 2000 (Biddle, 2000; Sorensen, 2000).

According to Safire (2008), it was an enormous BP media campaign in 2005 that gave a big

boost to wider use of the concept. By then, we can also see the term being using in the

scientific literature (e.g. Haefeli and Telnes, 2005). In the library of publications in the Web

of Science, the CF is mentioned for the first time in January 2007, in a letter to Nature

(Hammond, 2007).

Despite its popularity and use in commerce, there is no universally accepted

definition of CF. Today it describes the narrowest to the widest interpretation of GHG

emission measurement (East, 2008; Finkbeiner, 2009; Pandey et al., 2011; Peters, 2010;

Wiedmann and Minx, 2007). Although the Kyoto Protocol does not use the term (the

Protocol was conceived long before the CF), it would make some sense to be able to take

this formal international agreement as a reference for the definition of the CF, because

measuring GHG emissions is at the core of the Protocol. However, the Kyoto Protocol is

primarily a political construct, not a scientific effort to define in a comprehensive and

systematic manner how to quantify direct and indirect GHG emissions in relation to

activities, products and consumption patterns (e.g. it has openings to discount certain

emissions that intuitively should be counted).

The CF concept has been defined mainly by private organizations and businesses

(Kleiner, 2007; Wiedmann and Minx, 2007). The scientific community jumped on the train

in 2007, after the concept had already started to spread in business and commerce. The

most extensive survey on the definition of the CF was done by Wiedmann and Minx

(2007). Their research shows that the available studies do not offer uniformity in the

definitions and methodology of the CF. They suggest the definition of CF is ‘a measure of

the exclusive total amount of CO2 emissions that is directly and indirectly caused by an

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activity or is accumulated over the life stages of a product’. Pandey et al. (2011) describe

the CF as ‘the quantity of GHGs expressed in terms of CO2-equivalent, emitted into the

atmosphere by an individual, organization, process, product, or event from within a

specified boundary’. In both cases, the definition does not allow for subtractions as a result

of offsetting. In practice, however, companies tend to claim that carbon offsetting reduces

their CF. Furthermore, in practice it is not always clear whether CFs communicated refer

only to direct GHG emissions or indirect ones as well – scientists generally define the CF

of a product as including both direct and indirect emissions. Both in science and in practice,

the term is applied to different entities: single processes, whole supply chains (or all life-

cycle stages) of products, individual consumers, populations, companies, industry sectors,

and all sorts of activities and organizations.

6.2.2 The water footprint

The WF concept is primarily rooted in the desire to illustrate the hidden links between

human consumption and water use and between global trade and water resources

management (Hoekstra and Chapagain, 2007, 2008). The WF was developed as an analogy

to the EF concept. It was first introduced by Hoekstra in 2002 to provide a consumption-

based indicator of water use (Hoekstra, 2003). It is an indicator of freshwater use that

shows direct and indirect water use of a producer or consumer. The first assessment of

national WFs was carried out by Hoekstra and Hung (2002). A more extended assessment

was done by Hoekstra and Chapagain (2007, 2008) and a third, even more detailed,

assessment was done by Hoekstra and Mekonnen (2012a).

Unlike the CF, which emerged in practice, the WF was born in science. The WF

started to gain broad interest from about 2008, the year in which the Water Footprint

Network (WFN) was established – a network of academic institutions, governments, non-

governmental organizations, companies, investors and UN institutions. One of the aims of

the Network is to ensure the establishment of one common language and a coherent and

scientifically sound framework for Water Footprint Assessment (WFA) that serves different

interests; for example, WFA for products and companies, but also national WFA.


In 2009, about seven years after the first use of the WF concept, the WFN

published the first version of the global standard for WFA. Two years later the second

version was published (Hoekstra et al., 2011). This standard, which was produced in a

process of consultations with organizations and researchers worldwide and subjected to

scientific peer review, has comprehensive definitions and methods for WF accounting. It

shows how WFs are calculated for individual processes and products, as well as for

consumers, nations and businesses.

It also includes methods for WF sustainability assessment and a list of WF

response options. As could be expected, the definitions and methods have been challenged

(Wichelns, 2011), but no alternative methodological framework has been developed (unlike

in the case of the CF). The WFN standard contains definitions of the WF, of process steps,

products, producers and consumers, as well as of the WF within a geographically delineated

area. The WF is, in general, an indicator of freshwater appropriation, measured in terms of

water volumes consumed (evaporated or incorporated into a product) and polluted per unit

of time. The WF concept is further defined more specifically for a particular process or

product, and for any well-defined group of consumers (e.g. individual, family, village, city,

province, state, nation) or producers (e.g. public organization, private enterprise, economic

sector). From a producer and consumer perspective, the WF is an indicator of both their

direct and their indirect water use. The WF is a geographically and temporally explicit

indicator, showing not only volumes of water use and pollution, but also their locations.

6.3 Comparison of the carbon and water footprints from a methodological viewpoint

The carbon and water footprint concepts complement each other, addressing different

environmental issues: climate change and freshwater scarcity. Although there are

similarities in the way both footprints are defined and calculated, they differ in important

ways as well (Table 6.1). The location and timing within the year of GHG emissions, for

example, are not relevant, whereas location and timing of water consumption and pollution

matter critically. It is important to understand the similarities and differences between the

two footprints for formulation of wise policy responses. This understanding can help

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decision-makers recognize to what extent the type of mitigation policies that have been

formulated for one footprint can be applied to the other.

6.3.1 Environmental pressure indicators

Both the CF and the WF are ‘pressure indicators’ (Rotmans and De Vries, 1997; UNEP,

2012). Environmental pressure indicators measure the human use of natural resources and

the anthropogenic emission of compounds into the environment, but they do not show the

resulting change in the environment. The CF, for instance, shows GHG emissions, not the

resultant higher GHG concentrations in the atmosphere or the subsequent changes in

temperature, evaporation, precipitation or sea level. The WF shows the human consumption

and contamination of freshwater resources, not the resultant changes in runoff and water

quality in rivers and aquifers. As pressure indicators, the CF and WF show neither resultant

environmental changes nor final impacts of those environmental changes on human beings

(e.g. health) and ecosystems (e.g. biodiversity), but they are still useful measures of

pressure that humans put on the environment for policy-makers working to address

overexploitation of natural resources and the planet’s carrying capacity. Reduction

strategies concerning CF and WF fit within policy aimed to mitigate the causes of

environmental change and subsequent societal and ecological impacts. CF reduction, for

example, fits within a policy of climate change mitigation. For climate change adaptation,

other measures and indicators would need to be used. Similarly, WF reduction suits a

policy to lessen water scarcity and water quality deterioration. For coping with increased

water scarcity and contaminated water, other measures and indicators are better suited.


Table 6.1. Comparison of carbon and water footprints. Carbon footprint (CF) Water footprint (WF)

What is measured The anthropogenic emission of greenhouse gases (GHG).

The human appropriation of freshwater resources in terms of volumes of water consumed and polluted.

Unit of measurement

Mass of carbon dioxide (CO2)-equivalents per unit of time or per unit of product.

Water volume per unit of time or per unit of product.

Spatiotemporal dimension

Timing within the year and place of emissions are not specified. It does not matter where and when carbon emissions occur; carbon emission units are interchangeable.

WFs are specified in time and by location. It matters where and when a WF occurs; WF units are not interchangeable. For some uses, total/average WFs are shown, thus leaving out spatiotemporal specifications.

Footprint components

CF per type of GHG: CO2, CH4, N2O, HFC, PFC, and SF6. Emissions per type of gas are weighted by their global warming potential before adding.

Blue, green and grey WF. If added, the three components are added without weighting.

Entities for which the footprint can be calculated

Processes, products, companies, industry sectors, individual consumers, groups of consumers, geographically delineated areas.

Processes, products, companies, industry sectors, individual consumers, groups of consumers, geographically delineated areas.

Calculation methods

Bottom-up approach: - For processes, products and small

entities - The method of Life Cycle Assessment

(LCA) Top-down approach: - For sector, national and global studies - The method of Environmentally

Extended Input-Output Analysis (EE-IOA)

Hybrid approach: LCA and EE-IOA for products, nations, organizations

Bottom-up approach: - For processes, products and

businesses, but also for sector, national and global studies

- The method of bottom-up accounting in Water Footprint Assessment (WFA)

- For products, the accounting along supply chains in WFA is similar to the accounting in the Life Cycle Inventory stage of LCA studies

Top-down approach: - For sector, national and global

studies - The method of top-down

accounting in WFA, which is based on drawing national virtual water trade balances

- The method of EE-IOA is used as an alternative

Scope 1. Direct emissions 2. Indirect emissions from electricity used 3. Other indirect emissions

Always includes direct and indirect WF.

Sustainability of the footprint

Additional information is required to assess the sustainability of the CF. For the planet as a whole, a maximum allowable GHG concentration needs to be estimated, which needs to be translated to a CF cap. For specific processes and products, CF benchmarks can be used.

Additional information is required to assess the sustainability of the WF. Per catchment area, freshwater availability and waste assimilation capacity need to be estimated, which form a WF cap for the catchment. For specific processes and products, WF benchmarks can be used.

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6.3.2 Units of measurement

The CF is expressed in mass units (e.g. kg or tonnes) per unit of time (generally per year).

The CF of a product is expressed in mass units per unit of product. In cases in which only

CO2 is included in the calculation, the unit is kg CO2; if other GHGs are included, the unit

is kg CO2-equivalents (CO2-e). CO2-equivalents are calculated by multiplying the various

GHG emissions by their 100-year global warming potential. In most cases, the six GHGs

identified by the Kyoto Protocol are included in the analysis: CO2, CH4, N2O, HFC, PFC

and SF6. However, there is no common understanding and agreement of which gases should

be included in CF studies (East, 2008; Kleiner, 2007). The selection of gases depends on

the standard followed and the scope and type of the CF study. Although some studies

suggest to include only CO2 (Wiedmann and Minx, 2007), the common understanding and

direction in CF calculations is to include all six Kyoto Protocol gases (Pandey et al., 2011;

Peters, 2010).

The WF is measured in terms of water volume (e.g. L or m3) per unit of time (e.g.

day, month, year). A product WF is expressed as a water volume per unit of product. The

amount of product can be measured in various ways; for example, in terms of mass,

volume, number of pieces, monetary value or energy content. Mekonnen and Hoekstra

(2012) quantify and compare, for instance, the water footprint of various crop and animal

products in terms of L per kg, L per kcal, L per g of protein, and L per g of fat content.

6.3.3 Spatial and temporal dimensions

When determining CFs, GHG emissions are usually estimated with the help of emission

factors. Emission factors are available for a wide range of processes (WRI and WBCSD,

2004). Most CF studies are based on global average data on emissions per unit of good or

service. However, national emission factors have also been introduced to reflect divergent

local characteristics (Solomon et al., 2007). WFs provide spatiotemporally explicit

information on how water is appropriated for various human purposes. In WF accounting,

the approach is to use local productivities (Mekonnen and Hoekstra, 2011b, 2012).

Obviously, at the global level it does not matter whether footprint analysis is carried out on

the basis of local or global average productivities, because adding the results obtained with


local data will yield the same global result as an analysis based on global average data. But

on a national level, the result will differ when local productivities are used instead of global

averages.

It does not matter where and when carbon emissions occur; carbon emission units

are therefore interchangeable. This is fundamentally different for the WF: it matters where

and when a WF occurs. WF units are therefore not interchangeable. This is particularly

relevant in the discussion about offsetting. For example, the WF in one catchment cannot

be compensated for by offsetting activities to reduce the WF in another catchment.

6.3.4 Footprint components

The CF comprises as many components as GHGs that have been included in the analysis.

The emissions per type of gas are weighted by their global warming potential. In contrast,

the WF always consists of three components:

• Blue WF: The consumption of ‘blue’ water resources (surface water and

groundwater).

• Green WF: The consumption of ‘green’ water resources (rainwater stored in the

soil as soil moisture).

• Grey WF: This refers to pollution and is defined as the volume of freshwater

that is required to assimilate the load of pollutants based on existing ambient

water quality standards (Hoekstra et al., 2011).

‘Consumption’ refers to the loss of water from the available ground–surface water

body in a catchment area, which happens when water evaporates, is incorporated into a

product, or is transported to another catchment area or the sea.

The WF is often presented as one aggregate number; in that case, the three WF

components are added without weighting. It has been recognized that although this

approach may be sufficient for awareness raising, for the purpose of policy formulation it is

essential to clearly distinguish the three WF components. In its definitive form, the WF is a

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multidimensional indicator of water use, explicitly showing water consumption (green and

blue WF) and pollution (grey WF) as a function of space and time.

Some researchers from the LCA community have proposed adding WF

components after multiplying each with a local weighting factor to account for differences

in local impact, thus obtaining ‘litres water-equivalent’ (Pfister and Hellweg, 2009; Ridoutt

and Pfister, 2010a; Ridoutt et al., 2009). By taking blue water scarcity in a catchment as the

weighting factor, a blue WF in a water-abundant catchment would count less than a similar

blue WF in a water-scarce area. This idea of weighting was undoubtedly inspired by the

weighting of different GHGs in CF calculations, but this approach is based on a

misunderstanding of the water scarcity issue. The WF does not aim to reveal the local

hydrological impact of water consumption; it aims to measure the use of freshwater

resources, which is helpful in determining how to allocate water among competing

demands. One litre of water used does not become more or less than one litre according to

the degree of water scarcity in a catchment. Weighting the WF in two locations based on

local water scarcity is like weighting oil consumption in two locations based on the scarcity

of local oil reserves – it does not make sense (Hoekstra et al., 2011). Furthermore, if the

WF of a product or company were to be calculated by multiplying consumed volumes by

local water scarcity, another problem arises: because water scarcity in a catchment is

defined as the total WF in the catchment divided by the water availability, the WF of a

product produced in a certain catchment would increase (or decrease) if other users in that

catchment increased (or decreased) their WF. This way of measurement is counterintuitive

(i.e. how can you explain that ‘my WF depends on your WF’) and does not offer a proper

incentive for companies to reduce their WF – if companies would reduce their WF, they

would reduce the WF of others as well. Unfortunately, the idea of weighting water volumes

based on local water scarcity seems to be rather persistent in the LCA community (Berger

and Finkbeiner, 2010). The confusion is that some researchers in that community treat the

WF as an environmental impact indicator, while in fact it is an environmental pressure

indicator, measuring the intensity of resource use.


6.3.5 Entities for which the footprints can be calculated

The CF and WF are similar in that the concepts can be applied to a wide variety of entities.

In both cases, the basic building block is the footprint of a process. Based on the CF or WF

of a process, the CF or WF of a product can be calculated by summing the CFs or WFs over

the steps of its supply chain or life cycle. By summing the CFs or WFs of the products

produced or consumed, the CF or WF of a company, an industrial sector, an individual

consumer, or a group of consumers can be assessed. The total CF or WF occurring within a

certain geographically delineated area (e.g. the territory of a country) is obtained by

summing the CFs or WFs of the activities within that area. The WF concept has been

applied to assess the WF of national consumption from its inception on (Hoekstra and

Hung, 2002), while the CF concept originally was applied to products and has only more

recently been applied to national consumption (Hertwich and Peters, 2009).

6.3.6 Calculation methods

Although the CF is widely used as a yardstick, there is little uniformity in its calculation

methods. The main differences are in:

• the scope of the study (indirect emissions are often excluded)

• the gases included

• the weighting of these gases to arrive at CO2-equivalents

• the system boundaries chosen to determine how to truncate the analysis of

emissions in the supply chain

There is also no unanimity on whether offsetting is valid as a way to reduce CF,

and if so, how certain offsetting activities can be counted.

Alternative calculation methods and standards have been formulated by different

organizations (Kenny and Gray, 2009; Padgett et al., 2008; Pandey et al., 2011; Wiedmann

and Minx, 2007). At the product level, CF standardization has been under discussion and

several organizations have published their own guidelines and standards. The Publicly

Available Specifications 2050 of the British Standards Institution was one of the first

standards describing calculation methods for product CFs – they were first published in

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2008 and updated three years later (BSI, 2011). This standard describes the calculation of

GHG emissions of goods and services based on the LCA approach. Other standards in wide

use are the GHG Protocol of the World Resources Institute (WRI) and the World Business

Council for Sustainable Development (WBCSD) (2004) and their recently published

Product Life Cycle Accounting and Reporting Standard (2011). The International

Organization for Standardization (ISO) is currently developing a product CF standard

known as ISO 14067 (ISO, 2012a). Other ISO standards related to the CF are ISO 14040

on Life Cycle Assessment (ISO, 2006a) and ISO 14064 on Greenhouse Gases (ISO,

2006b). The Japanese Industrial Standards Committee published a Basic Guideline of the

Carbon Footprint of Products (JISC, 2009).

The three main approaches used to calculate CFs are the bottom-up, top-down and

hybrid approaches (Matthews et al., 2008; Peters, 2010; Wiedmann and Minx, 2007). The

bottom-up approach is based on LCA, a method that estimates the environmental impact of

products by ‘cradle to grave’ analysis. This method is mainly used for estimation of the CF

of products and small entities (Finkbeiner, 2009; Peters, 2010; Schmidt, 2009; SETAC,

2008; Sinden, 2009; Weidema et al., 2008). There are numerous examples of this method

being applied to the CF calculation of specific products: computers (O'Connell and Stutz,

2010), newspapers and magazines (Boguski, 2010), and animal products (Edwards-Jones et

al., 2009; Flysjö et al., 2011). Although the bottom-up approach produces results with a

relatively high level of precision, it is data-demanding and brings system boundary and

truncation problems (Wiedmann, 2009b).

The top-down approach is used for calculating the CF of large entities such as

sectors, countries and regions. Environmentally Extended Input-Output Analysis (EE-IOA)

is the main method for top-down calculations (Minx et al., 2009; Pandey et al., 2011;

Wiedmann, 2009b). Such analysis makes use of an economic input-output model, which

represents the interdependencies between different sectors and final consumption in the

national economy or between the sectors in different national economies. An input-output

model contains a matrix that shows how the output of one industry is an input to another. It

also includes imports and exports and final consumption. Inputs and outputs are expressed

in monetary terms: the model shows the value of economic transactions between different


sectors in an economy. A monetary input-output model can be extended with environment-

related information for each sector, such as its emissions and natural resource use, thus

allowing for EE-IOA. At the national level, EE-IOA is based only on national input and

output tables, which can bring significant errors into CF analysis (Minx et al., 2009). The

introduction of multi-regional input-output models has solved this problem. However, two

major challenges remain: (i) the relatively coarse schematization of the economy in input-

output models (whereby economic activities with rather different natural resource use and

emission intensities are part of one sector) and (ii) the approximation of (often unknown)

physical flows between sectors by the (known) inter-sector monetary flows (which ignores

the fact that traded goods and services between sectors are not homogeneous). National CF

studies based on EE-IOA have been carried out, for example, for the United Kingdom

(Druckman and Jackson, 2009; Wiedmann et al., 2010), Australia (Wood and Dey, 2009),

Japan (Nansai et al., 2009), Brazil (Machado et al., 2001), the United States of America

(Weber and Matthews, 2008) and China (Chen and Chen, 2010; Zhao et al., 2009). Global

assessments of national CFs have been carried out by Hertwich and Peters (2009) and

Wilting and Vringer (2009).

The hybrid approach to CF accounting combines the specificity of process analysis

(using LCA) with the system completeness of EE-IOA (Lenzen and Crawford, 2009). This

approach retains the detail and accuracy of the bottom-up approach (which is especially

relevant in carbon-intensive sectors). In the hybrid approach, first- and second-order

process data are collected for the product or service and higher order requirements are

covered by input-output analysis (Wiedmann and Minx, 2007).

In WF accounting, there is only one standard: the Global Water Footprint

Standard published by the WFN in 2009 and revised in 2011 (Hoekstra et al., 2011). This

standard covers comprehensive definitions and methods for WFA. WFA has four stages: (i)

setting goals and scope; (ii) accounting; (iii) assessing sustainability; and (iv) formulating

responses. The standard covers methods for the calculation of the WF of processes,

products, companies, consumers, and consumer groups (e.g. people of a nation), and also

includes guidelines for sustainability assessment and response formulation. The WFs of

single process steps form the basic building blocks of all WF accounts. The WF of a

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product, for example, is the aggregate of the WFs of the relevant process steps. The WF

within a geographically delineated area is equal to the sum of the WFs of all processes

taking place within that area (Hoekstra et al., 2011). According to the standard, offsetting

activities cannot be counted as WF reduction. Furthermore, the term WF can be used only

to refer to the sum of direct and indirect WFs, so that no confusion can arise as to the scope

of the term. Companies can refer to their direct (operational) WF, which excludes their

indirect (supply-chain) WF.

The ISO has taken the initiative, under its Technical Committee on Life Cycle

Assessment, to develop a standard related to the WF: ISO 14046 (ISO, 2012b). By its

position under the LCA committee, the scope will be limited to processes and products and

align to the LCA methodology as formulated in other ISO standards in the LCA field. By

focussing on procedural issues rather than calculation methods, the standard will probably

(and hopefully) not be in conflict with the Global Water Footprint Standard published by

the WFN.

There are two approaches for WFA: bottom-up and top-down (Hoekstra et al.,

2011). No hybrid approach has been developed, although recently there has been an

initiative in this direction (Ewing et al., 2012). The bottom-up approach can be used for all

sorts of WF accounts. When calculating the WF of products with the bottom-up approach,

the accounting over supply chains is done in the same way as in a Life Cycle Inventory in

LCA studies. There are product WF studies based on the bottom-up approach for a large

variety of crop products (Mekonnen and Hoekstra, 2011b) and farm animal products

(Mekonnen and Hoekstra, 2012). More specific product studies have been carried out for

cotton (Chapagain et al., 2006), coffee and tea (Chapagain and Hoekstra, 2007; Jefferies et

al., 2012), biofuels (Gerbens-Leenes et al., 2009b), pizza and pasta (Aldaya and Hoekstra,

2010), wheat (Mekonnen and Hoekstra, 2010c), soft drinks (Ercin et al., 2011), rice

(Chapagain and Hoekstra, 2011), soy products (Ercin et al., 2012a) and margarine (Jefferies

et al., 2012). The bottom-up approach can also be applied for the calculation of the WF of

companies, sectors, nations and regions. The WF of the consumers in a country, for

example, can be calculated by multiplying all the goods and services consumed by the


inhabitants of the country by the respective water needs for those goods and services

(Hoekstra and Mekonnen, 2012a).

The bottom-up approach is generic and precise and can be applied for all WF

calculations. However, it can be data-demanding, especially for large entities (as with the

CF bottom-up approach). For the calculation of the WF of sectors, provinces, nations and

regions, the top-down approach can be used as an alternative. This approach is based on

input data on WF per entity (e.g. sector, province, nation, river basin) and virtual water

flows between these entities. The classic way in which the top-down approach has been

applied is based on drawing virtual water balances of countries using trade data and data on

WFs of traded commodities (Hoekstra and Chapagain, 2007, 2008). Alternatively, the EE-

IOA is nowadays also applied for WF studies (Ewing et al., 2012).

In the classic top-down approach, the WF of the people living in a province, nation

or river basin is calculated as the total use of water resources in the area under

consideration plus the gross virtual water import into the area minus the gross virtual water

export. Virtual water import is the volume of water used in other countries to make goods

and services imported to and consumed within the country considered. Virtual water export

is the volume of water used domestically for making export products which are consumed

elsewhere (Hoekstra and Chapagain, 2007, 2008). The bottom-up and top-down

calculations theoretically result in the same figure, provided there is no product stock

change over a year. The advantage of the bottom-up approach is its precision. However, as

noted, it is data-intensive and depends on the quality of consumption data. The top-down

approach does not require consumption data, but it does require trade data and is therefore

vulnerable to the quality of that data (Van Oel et al., 2009). The top-down approach was

used in all of the early national WF studies, but recent studies tend to use the bottom-up

approach (Hoekstra and Mekonnen, 2012a; Ercin et al., 2012b).

Input-output modelling has been used as an alternative tool for top-down WF

calculations for sectors and nations (Daniels et al., 2011; Duarte and Yang, 2011). It has

been used mainly for national WF studies – China (Guan and Hubacek, 2007; Hubacek et

al., 2009; Zhang et al., 2011b; Zhao et al., 2009), Japan (Horie et al., 2011), Spain

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(Cazcarro et al., 2011) and Mexico (López-Morales and Duchin, 2011) – but also for areas

and cities – Andalusia (Velázquez, 2006; Dietzenbacher and Velázquez, 2007), Beijing

(Zhang et al., 2011a), Zhangye City (Wang et al., 2009) and for the Yellow River Basin

(Feng et al., 2012). A global study with a multi-regional input-output model was done by

Feng et al. (2011), who compared the top-down approach with bottom-up techniques.

6.3.7 Scope

For corporate CF accounting, three scopes have been defined (WRI and WBCSD, 2004):

• Scope 1 refers to the accounting of direct GHG emissions, which occur from

sources that are owned or controlled by the company (e.g. the emissions from

combustion in owned or controlled boilers, furnaces, vehicles).

• Scope 2 refers to accounting of indirect GHG emissions from the generation of

purchased electricity used by the company.

• Scope 3 refers to other indirect GHG emissions, which are a consequence of the

activities of the company, but occur from sources not owned or controlled by it

(e.g. extraction and production of purchased materials, transportation of purchased

fuels) (Matthews et al., 2008).

The distinction between direct and indirect is also made in WF accounting. The

total WF of a consumer or producer refers, by definition, to the sum of the direct

(operational) and indirect (supply-chain) WFs of the consumer or producer. Without

specification, the term WF refers to the sum of direct and indirect WFs. The distinction

between scopes 2 and 3 as applied in CF accounting is not useful in WF accounting.

6.3.8 Sustainability of the carbon and water footprints

As indicators of pressure on the planet, the CF and WF by themselves tell little about

impact. They need to be compared with the planet’s carrying capacity. The global CF needs

to be seen relative to the maximum sustainable global CF (the ‘carbon cap’), which depends

on the amount of GHGs that can be assimilated without causing more than a certain

maximum degree of global warming (Solomon et al., 2007). The sustainability of the WF

needs to be evaluated per river basin: the WF in a catchment needs to be seen relative to the


maximum sustainable WF in the area. This explains why it is relevant to know where the

WF is located. The maximum sustainable WF in a catchment depends on the runoff and

environmental flow requirements in the area (Hoekstra et al., 2011, 2012; Ridoutt and

Pfister, 2010b). The global maximum sustainable WF is equal to the sum of the local

maximum sustainable WFs. In order to have a more practical guide for assessing

sustainability at the level of individual processes and products, process- and product-

specific benchmarks for CF and WF can be developed (Groenenberg and Blok, 2002; Zwart

et al., 2010).

6.4 Comparison of responses to the carbon and water footprints

In response to the increasing concern about climate change, governments, businesses and

consumers are considering ways to decrease the CF of activities and products. The two

main response strategies are reduction and offsetting. Reduction refers to doing things in a

less carbon intensive way – achieved through increasing carbon efficiency by applying low-

carbon technology, which has less GHG emission per unit of production – or ceasing

certain activities of production or consumption altogether. Offsetting refers to taking

external actions to compensate for a certain CF by means of some form of carbon capture

or reduction elsewhere by others. If the CF of a certain activity is offset 100%, it is

sometimes claimed that the activity is ‘carbon neutral’. The concepts of carbon offsetting

and neutrality are applied and supported widely by business, government and individual

consumers (Kollmuss et al., 2008; Moss et al., 2008; Murray and Dey, 2009).

Whereas various CF reduction and offsetting mechanisms have already been

developed and implemented, WF response mechanisms are still being explored. The broad

public interest in the WF is more recent than the interest in the CF. It is not surprising that

the same types of policy response that have been developed for the CF are now proposed

for the WF, and there are many analogous terms in the two fields: CF reduction vs WF

reduction; carbon efficiency vs water efficiency; carbon offsetting vs water offsetting;

carbon neutral vs water neutral; carbon cap vs water footprint cap; carbon permits vs water

footprint permits; and carbon labelling vs water labelling. All of these concepts are new in

the field of water resources management except for ‘water efficiency’, which has been

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applied for decades – but even this takes on a new dimension: whereas it generally referred

to water productivity at field level or within a factory, a supply-chain perspective is now

added.

Cross-fertilization occurs when insights and concepts from the sphere of climate

change mitigation are translated to the sphere of water. This can be fruitful, but also bears

risks. Water is not the same as carbon, so it should be questioned whether solutions for

carbon can be copied for water. Furthermore, not all ‘solutions’ that have been developed

for carbon appear to be effective, so they should be critically evaluated before being applied

elsewhere. Hoekstra (2008) notes that, undoubtedly, there will be a great market for water

offsetting and water neutrality, comparable to the market for carbon offsetting and

neutrality, but the extent to which this market will become effective in contributing to a

more efficient, sustainable and equitable use of the globe’s water resources will depend on

the rules of the market. Without agreed definitions and guidelines on what counts as water

offsetting and neutrality, the terms are most likely to end up as catchwords for raising funds

for charity projects in the water sector rather than as effective means to achieve measurable

overall WF reductions.

6.4.1 The need for reduction: Maximum sustainable footprint levels

There is a general acknowledgment that humanity’s CF and WF have surpassed sustainable

levels and that society must make efforts to reduce them, but it appears to be quite difficult

to establish unambiguous and agreed upon maximum sustainable levels for these footprints.

Knowing their ceilings is instrumental in formulating reduction strategies. The maximum

sustainable level for the global anthropogenic CF depends on the maximum allowable

global temperature increase, which in turn depends on the societal and ecological impacts

that are expected at different degrees of global warming. At the United Nations Climate

Change Conference in Copenhagen in 2009, note was taken of the scientific view that the

increase in global temperature should be below two degrees Celsius. If governments would

sign up to such a target – which they did not do – there would be a basis for establishing a

maximum concentration of GHGs in the atmosphere, and then a maximum CF in order to

remain below this maximum concentration. This in itself is not an easy task. The challenge


has long been framed as one of stabilizing GHG concentrations at particular levels, such as

550, 450 or even 350 parts per million (p.p.m.) CO2-equivalents.

Recently, several researchers have proposed an alternative view, in which the

mitigation challenge is framed as that of putting a cap on total cumulative GHG emissions

since the start of the industrial revolution (Allen et al., 2009; Matthews et al., 2009). This

proposal is built on the insight that the total allowable emissions for climate stabilization do

not depend on the timing of those emissions. It has been estimated that the peak warming

above pre-industrial temperatures would be limited to two degrees Celsius with a 50%

probability of success if cumulative CO2 emissions are capped at 1000 trillion tonnes of

carbon, more than half of which already has been emitted (Allen et al., 2009; Raupach,

2009). From this perspective, the maximum sustainable CF cannot be formulated as a

certain ceiling to the annual CF, but should be seen as a maximum budget we can spend

between today and, say, the end of this century – which means that the maximum CF

should continually decline and ultimately reach zero.

But even before this new insight on the required cap to humanity’s CF, there was

already broad scientific consensus that anthropogenic GHG emissions are currently far

beyond the level required to achieve a maximum of two degrees Celsius global warming

(Solomon et al., 2007). Although the commitments made by governments in the Kyoto

Protocol to reduce national GHG emissions by certain percentages are not nearly sufficient

in the view of a two-degree target, the idea of setting a cap to GHG emissions has been

institutionalized, which is probably the biggest achievement of the Protocol. Future focus

should be on sticking to that idea and further negotiating the level of national caps (and

even reducing caps over time), and on the mechanisms to be installed to ensure that caps

are not exceeded.

In contrast, even the idea of a maximum sustainable WF has not yet been

politically debated. As in the case of the CF, it is not easy to define what the maximum

sustainable WF of humanity is – and for the WF, another level of complexity is that the

maximum sustainable global WF is the sum of the maximum sustainable WFs in all the

river basins of the world. Furthermore, timing within the year is a factor. As shown by

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Hoekstra et al. (2012), unsustainable WFs become manifest during certain periods of the

year (generally when water availability is relatively low while the WF is relatively large),

so maximum sustainable WFs have to be established per catchment on a monthly rather

than an annual basis. Little research has been done on assessing the maximum sustainable

global WF. Ridoutt and Pfister (2010b) argue that the global WF must be reduced by about

half to reach a sustainable level of water utilization and they consider such a target realistic

given the potential for water productivity improvements in agriculture and industry and the

steps that could be taken to limit food chain waste.

A question often posed in the context of WF reduction is whether it is relevant to

reduce WFs in water-abundant river basins (e.g. Wichelns, 2011; Ridoutt and Huang,

2012). Reducing the aggregate WF in the most water-scarce catchments deserves priority

indeed, but this requires global action. As argued by Hoekstra and Mekonnen (2012b), an

important component of the solution to overexploitation of blue freshwater resources in

water-stressed catchments is to increase water productivities (reduce product WFs) in

water-abundant areas. Because water-intensive commodities can be traded internationally,

wise allocation of freshwater resources to alternative purposes is a question with a global

dimension (Hoekstra, 2011). Water-abundant areas often show low water productivities

(tonnes per m3) and thus large product WFs (m3 per tonne). Even though the local

environmental impacts of water use in these areas can be small, it would be a mistake to

leave them out of the scope of water policy.

6.4.2 Reduction of footprints by increasing carbon and water efficiency

Carbon efficiency is a popular term referring to the CF per unit of Gross Domestic Product

(GDP) in an economy, or more specifically to the CF of specific sectors or activities,

always per unit of production. A related term is energy efficiency. Companies and

governments usually translate the need for CF reduction into a need to increase energy

efficiency in industry, transportation and households, assuming that decreased energy use

per unit of good or service produced automatically translates into reduced GHG emissions.

There is also the recognition that we need to shift from carbon-intensive forms of energy


like coal and oil to less carbon-intensive forms like gas or, even better, renewable forms of

energy like wind, solar, hydro or bioenergy.

Although the strategies of increasing energy efficiency and shifting to renewables

seem quite straightforward, they are not always as effective in reducing GHG emissions as

we would expect. In practice, increasing energy efficiency does not necessarily correlate to

an overall reduction in energy use. More efficient production means that the same can be

produced with less energy, but it also means that more can be produced with the same

energy. Increased energy efficiency may thus contribute to increasing levels of production

and consumption. This is called the ‘rebound effect’, which describes increases in resource

or energy efficiency that do not result in a corresponding decrease in resource or energy use

(Berkhout et al., 2000). Many researchers have addressed this issue and concluded that

increasing energy efficiency will not be sufficient for reaching GHG emission reduction

targets (Binswanger, 2001; Birol and Keppler, 2000; Brännlund et al., 2007; Herring and

Roy, 2007; Hertwich, 2005; Roy, 2000). Whether a shift from fossil fuels to renewable

energy will result in a corresponding decrease in the CF can be questioned in a similar way.

Many renewable energy projects concern investments in energy production for new

activities; such projects may simply add to the total energy use and not replace fossil energy

use.

The feasibility of achieving increased carbon efficiency depends on available

technology, market conditions, and the role governments play in promoting the shift

towards a low-carbon economy. The IPCC distinguishes between three different ‘emission

reduction potentials’ (Metz et al., 2007):

• Market potential is the reduction potential based on private costs and private

discount rates. It reflects what is possible from a microeconomic perspective.

• Economic potential is the reduction potential based on social costs and benefits

and social discount rates. It reflects what is feasible from a macroeconomic

perspective.

• Technical potential is the amount by which it is possible to reduce GHG emissions

by implementing a technology or practice that has already been demonstrated. It is

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not limited by cost constraints, but by practical and physical limits, such as the

available technologies and the rate at which these technologies may be employed

(Van Vuuren et al., 2009).

The IPCC distinction between market, economic and technical potential for CF

reduction can be a useful approach in the discussion of WF reduction. What is technically

possible regarding WF reduction receives some attention in the Water Footprint Assessment

Manual. It introduces the terms ‘zero blue WF’ and ‘zero grey WF’ for the industrial sector,

referring to the possibility in most industries to fully close the water cycle and nullify

chemical loads to ambient water bodies (Hoekstra et al., 2011). The huge variation in WFs

for crop production shows that there is substantial potential for productivity increase and

WF reduction (CAWMA, 2007; Mekonnen and Hoekstra, 2011a; Zwart et al., 2010).

Examples of increased water efficiency in agriculture are use of drip irrigation instead of

sprinklers (reducing the blue WF) and replacement of conventional by organic farming

(reducing the grey WF). It would be useful to develop WF benchmarks for various

activities (processes) and end products in order to set WF reduction targets by process and

product.

The rebound effect discussed for the CF can be relevant when increasing water

efficiency (McGlade et al., 2012). Reducing the WF of activities in a river basin will

contribute to lessening the pressure on the basin’s water resources only when the reduced

WF per unit of activity is not nullified by a simultaneous increase in production.

6.4.3 Reduction of footprints by changing production and consumption patterns

It is acknowledged that increasing efficiencies can be only part of the solution for reducing

carbon and water footprints. Existing production and consumption patterns carry an

inherent dependence on energy and water that cannot be addressed by increasing

efficiencies alone. On the production side, for example, the international character of many

supply chains leads to an inherent dependency on energy for transport. The energy demand

can be reduced only if the supply chains are restructured such that less long-distance

transport is involved. Existing production patterns are often inherently water-intensive as


well; a good example is the common practice of intensive crop production in areas that are

short of rain. The blue water footprint of crops can be reduced only if worldwide crop

production is better aligned to where there is sufficient rain. Consumption patterns need

attention as well. The relatively large contribution of meat and dairy consumption to

humanity’s CF – Steinfeld et al. (2006) estimate that the livestock sector is responsible for

18% of anthropogenic GHG emissions – can be reduced only if people reverse the current

trend towards eating more meat and dairy. Replacement of a meat-heavy meal by a

vegetarian or a meat-light meal will also help to substantially lower the WF (Mekonnen and

Hoekstra, 2012). Not using first-generation biofuels or at least avoiding biofuels from the

most water-intensive crops will help as well (Gerbens-Leenes et al., 2009b).

A reconsideration of production and consumption patterns is much more difficult

than implementing measures to increase efficiencies because structural changes affect all

sorts of vested interests, while, at least in the short term, efficiency gains benefit all parties.

This explains why most of the attention of footprint reduction goes to efficiency and not to

total production and consumption volumes. Both producers and consumers generally want

to increase the levels of production and consumption, and efficiency gains can be

instrumental in that. Because of the rebound effect, CF and WF reduction strategies that are

focused on efficiency are likely to fail. Carbon and water efficiency increases need to be

coupled with caps on total CFs and WFs.

6.4.4 Offsetting, neutrality and trading

The idea behind carbon offsetting is that one unit of CO2-equivalent emitted into the

atmosphere in one place from one activity has exactly the same contribution to climate

change as another unit emitted elsewhere by another activity. As a result, a certain emission

reduction always has the same effect, no matter how or where it is done (Bellassen and

Leguet, 2007). Furthermore, there is the underlying idea that one can better reduce an

emission elsewhere – if it is easier or cheaper – than reduce one’s own emission (Bumpus

and Man, 2008).

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The practice of carbon offsetting was developed from the flexible mechanism

included in the Kyoto Protocol that allows industrial countries to fulfil their obligations to

reduce GHG emissions by purchasing emission reductions created by projects elsewhere

(Barker and Ekins, 2004; Viguier et al., 2003). This mechanism was created as a result of a

market logic, where demand and supply for reductions are created, priced and exchanged

internationally and developed further with a parallel voluntary market. A typical example of

the voluntary market can be found in the air transport sector: passengers can offset the

emissions related to their flight by purchasing reduction credits elsewhere. Another

example is offsetting emissions of energy use by buying carbon credits that are generated

by renewable energy or forest planting projects (Bellassen and Leguet, 2007; Bumpus and

Man, 2008).

Although the offsetting concept is based on some logic, it has unanswered

questions that create confusion. Measuring, accounting and verifying are the main

concerns, especially in voluntary offsetting. There are no clear definitions of what can

count as an offset and no standardized methods to calculate the amount of CF that can be

compensated for by a certain offset activity. Murray and Dey (2009), in their study of

commercial websites that offer carbon offsets to companies and individuals, found that

these enterprises do not have similar values for required offsets; do not have the same

inputs and calculation methods; and, even for CF values that are close, do not have the

same pricing of the offsets. They concluded that lack of standardization and transparency

are the main problems in today’s voluntary offset market. Another concern about offsetting

is the credibility of sequestration and other carbon credit projects. Finally, offsetting allows

polluters to continue emitting, which is the wrong signal to be spreading regarding CF

reduction. Together, these concerns place offsetting in a bad light. And there are many

indications that both the formal (Kyoto Protocol) and voluntary mechanisms of offsetting

have little effect on overall CF reduction (Spash, 2009). The absence of a closed accounting

system makes it very difficult to measure the effectiveness of the whole system.

The idea of water offsets (or water credits) is gaining ground in the water

community. However, as for carbon offsetting, the concept of water offsetting is still ill-

defined. According to Hoekstra et al. (2011), in general terms it means taking measures to


compensate for the negative impacts of the WF that remain after WF reduction measures

have been implemented. But the two weak points of the definition are that (i) it does not

specify which compensation measures and what level of compensation are good enough to

offset a certain WF impact and (ii) it does not specify which impacts should be

compensated and how to measure these impacts. An ill-defined concept can be easily

misused – measures taken under the banner of ‘offsetting’ can potentially be a form of

‘greenwashing’ rather than a real effort aimed at full compensation. Another problem is that

WFs and their associated impacts are always local; as has already been discussed in this

report, in this respect the WF is markedly different from the CF. The idea of a global offset

market does not make sense for water as it does for carbon. An offset for a WF should

always occur in the catchment where the WF is located. This brings attention back to a

company’s own WF and does not allow it to simply buy an offset in a general

compensation scheme (Hoekstra et al., 2011).

6.4.5 The interplay of actors

The challenge of CF reduction lies on the plate of various actors: governments, companies,

investors, individual consumers and intergovernmental forums. To limit or reduce GHG

emissions, national governments have been using various policies and measures: setting

regulations and standards, applying taxes and subsidies, creating carbon credit markets,

promoting voluntary actions, instigating research programmes and developing

communication tools (Bumpus and Man, 2008; Kollmuss et al., 2008; Koteyko et al., 2010;

Metz et al., 2007; Solomon et al., 2007; Stewart and Wiener, 2004; Wara and Victor, 2008).

Four criteria are generally applied to evaluate the usefulness of each instrument: (i)

environmental effectiveness; (ii) cost-effectiveness; (iii) distributional effects (including

equity); and (iv) institutional feasibility (Harrington et al., 2004; Metz et al., 2007). It is

important to note that CF-specific policies are not enough to reach CF reduction goals.

Policies on poverty reduction, land use, trade, pollution, agriculture, food security and

population should be considered altogether.

Regulation, legislation and standards are typical instruments used in

environmental policy. The effectiveness of regulatory measures and standards depends on

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their stringency. They can be very effective and useful tools when businesses and

consumers do not respond to calls for voluntary action. In the field of CF reduction, such

policy instruments have successfully been implemented to promote energy efficiency: the

European Union’s action for the aviation industry and the US action for registry of

emissions under the Consolidated Appropriations Act (2008) are two good examples of

how regulation can play an important role (Courchene and Allan, 2008; Pandey et al.,

2011). Several more examples can be found for the role of legislation, such as California’s

Global Warming Solutions Act (2006), which aims to reduce emissions and promote

capping (Kossoy and Ambrosi, 2010), and the UK’s Low Carbon Transition Plan (DECC,

2009). These examples show that regulatory standards are valuable in emission reduction.

They are effective in stimulating consumers and industries to reduce their footprints.

In addition to regulatory intervention, governments can intervene in markets by

applying taxes and subsidies, and they can promote consumption patterns that contribute to

emission reduction. Taxes on emissions can be effective in terms of both environmental and

cost concerns; for example, taxation in Denmark resulted in a 6% reduction and in Norway

decreased emissions per unit GDP (Bruvoll and Larsen, 2004). However, they can create

distributional and institutional problems (Metz et al., 2007). Taxes can also be ineffective

for overall reduction as they provide polluters with an alternative: pay tax and pollute

instead of invest in emission reduction. Furthermore, taxes are not popular policy tools, and

political constraints and lobbying by industry can make them difficult to implement.

Financial incentives are policy tools commonly used by governments to stimulate new

technologies. Taxation and market creation also have important roles in technology

development and innovation.

Through governmental regulations and policies, companies have started to realize

that we are moving towards a carbon-constrained economy (Kleiner, 2007), and they are

aware that they will soon face taxation, capping and other regulations related to their GHG

emissions. CF calculation and emissions reduction is nowadays high on the agenda of many

businesses. The main driver behind their rush to react is to enable continuation of their

activities in a carbon-constrained economy and navigation of the new landscape to their

advantage. But it is also a reaction to broad public concern over climate change and


changes in consumer behaviour – a survey done in the UK showed that 44% of consumers

are willing to pay more for low CF products (Pandey et al., 2011). Companies can react to

all of these changes; they can see the new business opportunities in a carbon-based

economy and create new markets for themselves: carbon trading, consulting, calculating,

offsetting, and so forth. The role of business in strategies towards reduction of emissions is

significant. Companies can change their production systems and invest in low-carbon

technologies, but the financial burden associated with these actions can be immense and

companies are not necessarily willing to take on this burden without legislation and changes

in consumer choices pushing them to do so.

There is no doubt that communication tools are effective in CF reduction, but they

are indirect and thus their effects are hard to quantify. Governments can use awareness and

education campaigns to promote sustainable consumption and help consumers make better-

informed choices. They can also influence producers to make production more sustainable

(Stevens, 2010). In the case of the CF, communication instruments such as product

labelling, carbon disclosure and public awareness campaigns are under discussion and

several initiatives have been taken.

Carbon labelling of products is one of the tools that companies are starting to use

to share CF information with consumers to help them make better-informed choices. Some

governments, for example the French, are starting to think about regulation of product

labelling. If labelling schemes are well defined and structured and use credible information,

labelling could be an effective tool for creating incentives to move towards low-carbon

products and supply chains (Brenton et al., 2009). Unfortunately, today’s CF does not

provide such credibility because it has neither a standard definition nor a standard method

of calculation.

With the growing awareness of global warming, individuals have become more

concerned about their own actions. Individuals can lower their CFs by lowering their

energy use at home and adapting their consumer and other behaviours; for example, buying

locally grown food, travelling less, and travelling by bicycle or public transport (Frank et

al., 2010; Kollmuss et al., 2008).

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As can be clearly seen from the discussion above, societal response to the CF

involves many actors taking their own steps – and by doing so they influence one another,

which is an essential element in the overall response. A similar diversity of actor initiatives

and mutual influences will probably develop for the WF, but we are at too early a stage to

be able to reflect on the various governmental, corporate and civil society initiatives that are

currently being taken in the WF field. The Spanish Government has made WF analysis

mandatory in the preparation of river basin plans. Many other governments, for example

that of South Africa, are in an exploratory stage (Hastings and Pegram, 2012). A great

number of companies, most of them multinationals (e.g. Unilever [Jefferies et al., 2012]),

have started to compute the WF for some of their products and to explore response

strategies. More and more WF calculators are appearing online, the media is picking up the

concept, and environmental organizations (e.g. the World Wildlife Fund and The Nature

Conservancy) are starting to use the concept in their awareness campaigns.

Based on experience with the CF, it is hard to imagine progress in WF reduction

without strong governmental and intergovernmental leadership. Legislation, regulation and

standards will probably be necessary to stimulate consumers and industries to reduce their

WFs. It will be important that the different WF components are treated individually, and in

particular, strict regulations regarding the blue and grey WFs will be necessary to ensure

optimal use and allocation of scarce water resources. Taxation can be a policy instrument;

however, in reality taxation on one specific criterion is rare and politically very difficult to

implement. Subsidies and financial incentives can be helpful instruments to promote new

technologies and innovations, efficient use of water, reuse and recycling of water, and

better wastewater treatment.

6.4.6 The water–energy nexus

There is a growing recognition that water policy and energy policy must be somehow

related, because energy production requires water, and water supply requires energy. In the

past, in fact until today, water and energy policies have mostly been disconnected. Whereas

efforts have been undertaken to improve both water use efficiency and energy efficiency,

we can observe two interesting trends. First, the water sector is becoming more energy-


intensive – think, for example, of the energy needed for pumping groundwater from deeper

and deeper sources, for constructing large inter-basin water transfer schemes and moving

water through them, and for desalination of saltwater or brackish water. Second, the energy

sector is becoming more water-intensive – especially because of the increasing focus on

biomass as a source of energy (Gerbens-Leenes et al., 2009b). All energy scenarios for the

coming decades show a shift towards an increased percentage of bioenergy, and thus an

increasing WF (Gerbens-Leenes et al., 2012). The challenge is to search for coherent

policies that reduce both CF and WF rather than developing energy policies that reduce CF

but increase WF (like first-generation biofuels) and water policies that reduce WF but

increase CF (like desalination).

6.5 Lessons to learn

As has been highlighted throughout the report, the CF and WF fields can inform each other

in standardization, development, credibility, reduction strategies and policy tools. The main

messages and lessons from the study of both concepts can be summarized as follows:

Definitions and methods

The use of the same definitions and methods for each of the CF and the WF across

countries and sectors lends credibility to the concepts and is a good basis for setting real

reduction targets and being able to verify them. The CF currently has competing and

conflicting standards; standardization has failed due to a lack of coordination. In the case of

the WF, the efforts of the Water Footprint Network to form a broad coalition of partners

and develop a science-based global WF standard in an early stage of its practical use have

been successful. The risk of future confusion from potentially competing initiatives (e.g.

ISO [2012b]) is nevertheless present for the WF.

Reduction schemes

Reduction of the CF and WF through increasing carbon and water efficiencies is important,

but the rebound effect must be given due attention. In energy studies, this effect is well

known; in water studies the effect has had little attention to date. Alongside efforts to

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improve efficiencies, efforts to make societies less energy- and water-dependent are an

essential ingredient of a good reduction policy.

Offsetting schemes

Offsetting schemes have inherent problems. The offsetting concept is ill-defined and can

easily be misused, as illustrated in the sphere of CF offsetting. Without a clear definition,

measures taken under the banner of ‘offsetting’ can potentially be a form of ‘greenwashing’

rather than a real effort aimed at full compensation. An offset of a WF should always occur

in the catchment where the WF is located and in the period when it happens. This means

that thinking in terms of general compensation schemes where one can simply ‘buy’ an

offset is not applicable to the WF. In sum, offsetting is not a good option for a water

scarcity mitigation strategy.

Regulatory standards

Regulatory standards have been useful and valuable for emissions reduction related to the

CF, and governments should be aware that regulation can be an effective instrument in WF

reduction as well. Regulation should aim to drive consumers and industries towards

reducing their WF. Particularly strict regulations on reducing the blue and grey WF

components can play a crucial role in optimal use and allocation of scarce freshwater

resources, something that would be hard to achieve with awareness raising programmes and

voluntary action alone.

Taxation

In theory, taxation could be a useful policy instrument in WF reduction strategies; however,

as experience with the CF has shown, specific taxation on one criterion is rare and

politically difficult to implement. Taxation in the WF area will also have additional

complexity in implementation due to distributional problems. In sum, taxation does not

look like a wise policy tool for WF reduction.


Multi-dimensional policies

For WF reduction, as for CF reduction, policies that address poverty, land use, trade,

pollution, agriculture, food security and population should be considered together. CF- and

WF-specific policies in isolation are not sufficient to meet reduction goals.

Product labelling

Although the CF and WF concepts can be used in product labelling as a communications

tool to raise consumer awareness, their actual figures do not have sufficient information to

allow consumers to make well-informed decisions on which products and services to

purchase preferentially. Both footprints need to be compared to benchmarks, and for the

WF, location and timing is relevant as well. Consumers are likely better served by labels

that grade the sustainability of a product from low to high – criteria regarding the CF and

WF can be integrated into such designations.

Leadership by government

Experience with the CF shows that for the development of comprehensive policy responses

for WF reduction, strong governmental leadership and action will be required. Commitment

and regulation are required at the national and international level. Engagement of business

through production systems and individuals through consumer behaviour are also essential

elements of policy response.

6.6 Conclusion

The CF has become a widely used concept by society, despite its lack of scientifically

accepted and universally adopted guidelines. Stakeholders use the term with loose

definition, according to their liking. The WF is becoming popular as well, and there is

substantial risk that it will suffer the same problems as the CF. By attempting to understand

the mechanisms behind the societal adoption of the CF, this report extracts lessons that may

help reduce the risk of the WF losing its strict definition and interpretation.

Reduction and offsetting mechanisms have been applied and supported widely in

response to the increasing concern about global warming. However, the effective reduction

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of humanity’s CF is seriously challenged because of three factors. First, the absence of a

unique definition of the CF means that reduction targets and statements about carbon

neutrality are difficult to interpret; this leaves room for developments appearing better than

they really are. Second, the focus on increasing carbon efficiency bears the risk of the

rebound effect. Third, existing mechanisms for offsetting are extremely weak; it remains

questionable whether or to what extent they actually contribute to the overall reduction of

GHG emissions.

Responses for WF reduction are still under question. Water offsetting strategies

will face the same problems as those of carbon, but there is a further problem: water

offsetting can only be effective if it takes place at the specific location and in the specific

period of time when the WF that is to be offset took place. The weakness of offsetting and

neutrality mechanisms for the CF shows that applying those concepts to the WF is not a

good idea. A more effective tool is probably direct WF reduction targets to be adopted by

both governments and companies.

7 Integrating ecological, carbon and water footprint into a “footprint family” of indicators: definition and role in tracking Human Pressure on the Planet 6

Abstract

In recent years, attempts have been made to develop an integrated footprint approach for

the assessment of the environmental impacts of production and consumption. In this paper,

we provide for the first time a definition of the “footprint family” as a suite of indicators to

track human pressure on the planet and under different angles. This work has been

developed under the 7th Framework Programme in the European Commission (EC) funded

One Planet Economy Network: Europe (OPEN:EU) project. It builds on the premise that no

single indicator per se is able to comprehensively monitor human impact on the

environment, but indicators rather need to be used and interpreted jointly. A description of

the research question, rationale and methodology of the ecological, carbon and water

footprint is first provided. Similarities and differences among the three indicators are then

highlighted to show how these indicators overlap, interact, and complement each other. The

paper concludes by defining the “footprint family” of indicators and outlining its

appropriate policy use for the European Union (EU). We believe this paper can be of high

interest for both policy makers and researchers in the field of ecological indicators, as it

brings clarity on most of the misconceptions and misunderstanding around footprint

indicators, their accounting frameworks, messages, and range of application.

7.1 Introduction

7.1.1 Global environmental change: an overview

In the last few decades countries around the world have significantly changed, most

experiencing economic growth, poverty reduction, and improved welfare (UNDP, 2006;

UNEP, 2007). Such economic and social changes have been reached at the expense of the

6 Based on Galli et al. (2012)

210/ Chapter 7. Footprint family of indicators

planet’s ecosystem preconditions and its ability to sustain life as demand for ecological

assets has been increasing, standards of living improving and the size of the global

economy and population growing unabated (Fisher-Kowalski and Haberl, 2007; Goudie,

1981; Haberl, 2006; Nelson et al., 2006; Rockström et al., 2009).

Global resource consumption and waste emissions have grown to a point where

humanity now consumes at a faster pace than the Earth can regenerate or sequester (Catton,

1982; Ehrlich, 1982; Haberl et al., 2007; Hoekstra et. al, 2009; Meadows et al., 1972;

Vitousek et al., 1986; Wackernagel et al., 2002; WWF, 2008;), and this mounting human

pressure is at the root of many of the most pressing environmental problems and many of

the factors contributing to the current global food crisis.

Many forests, particularly in tropical zones, are cut faster than they can regrow:

130,000 km2 of forest have been destroyed per year for the last 15 years. Fish are caught

faster than they can restock: 15% of ocean stocks were depleted in the same period (UNEP,

2007). World average per capita food and services consumption has grown during the last

four decades (Turner, 2008); global extraction of natural resources (e.g., biomass, fossil

fuels, metal ores, and other minerals) has increased by nearly 45% in the last 25 years

(Behrens et al., 2007; Giljum et al., 2009a; Krausmann et al., 2009) coupled with a

quadrupled world population over the last one hundred years. Many countries in arid and

semi-arid regions of the world, especially Central and West Asia and North Africa are

already close to or below 1000 m3 capita-1 year-1, defined as threshold for water scarcity

(Falkenmark, 1989).

Greenhouse gas (GHG) emissions are accumulating in the atmosphere, causing

climatic changes and potential negative feedback on the health of ecosystems (Butchart et

al., 2010; Haberl, 2006; Holdren, 2008; UNEP, 2007). Worldwide atmospheric

concentrations of carbon dioxide (CO2), Methane (CH4), and nitrous oxide (N2O), for

example, have noticeably increased in recent decades, and they now considerably exceed

the natural range over the last 650,000 years. With high confidence, scientists have

concluded that these global average concentrations are due to human activities (IPCC,

2007).

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In addition, the distribution of human-induced pressures is uneven in both its

nature (Behrens et al., 2007; Haberl, 2006; Krausmann et al., 2009) and its geographic

location (Erb et al., 2009a; Foley et al., 2005; Giljum et al., 2009a; Haberl et al., 2007;

Halpern et al., 2008; Hertwich and Peters, 2009; Hoekstra and Chapagain, 2007; Kitzes et

al., 2008a; Moran et al., 2008; Ramankutty and Foley, 1999; Ramankutty et al., 2002). On a

per capita basis, people in high income countries consume much more natural resources

than those in lower income countries as they are characterized by a higher affluence and an

easier geographical and economic accessibility to resources. The transition from biomass-

driven (agricultural) to fossil-fuel-driven (industrial) societies experienced by many high

income countries (Haberl, 2006) has also determined a shift in the nature of the resources

currently demanded and the ecosystem compartments that are now under the highest

human-induced pressure.

As scenarios illustrate, these trends will likely continue in the future if measures

are not taken to reduce this demand. For example, in a business-as-usual scenario, global

extraction of natural resources could further grow by more than 50% by 2030 compared to

today’s situation (Lutz and Giljum, 2009), and humanity’s demand on ecological assets (in

ecological footprint terms) could equal two Earths worth of resources by 2040 (Moore et

al., 2012). Up to two-thirds of the world population will then experience water scarcity over

the next few decades (Alcamo et al., 2000; Vorosmarty et al., 2000) and slightly more than

one billion people living in arid regions will face absolute water scarcity (less than 500 m3

capita-1 year-1) by 2025 (Rosegrant et. al, 2002).

Acknowledging the increasing human impact on the natural world, more empirical

measurements have thus to be sought to understand the driving forces behind these impacts

and find ways to reduce impacts while maintaining economic and societal well-being.

The EC funded One Planet Economy Network: Europe (OPEN:EU) project, under

which this work has been performed, originates from exactly this willingness to provide

policy makers, particularly at EU level, with a tool that can help addressing the objectives

of the EU Sustainable Development Strategy (SDS) and other policy strategies. This will


help create a new forum for the visions, knowledge and interests of different stakeholders to

help transform the EU into a One Planet Economy by 2050 (OPEN:EU, 2010).

7.1.2 The need for a set of indicators

As human societies and economies depend on the biosphere's natural capital for many

underpinning functions (Best et al., 2008; Levin and Pacala, 2003), managing the planet’s

ecological assets must become a central issue for decision makers around the world.

Despite an impending urgency to act, actions must be based on scientifically-sound

accounting and integrated ecosystem approaches can potentially best inform decision

makers as they enable to tackle multiple issues concurrently, and help avoid additional

costs and inadvertently undoing progress in one sector by not accounting for direct and

indirect implications of actions in another sector (Robinson et al., 2006; Turner, 2008). For

this reason we believe a set of indicators is needed to account for the environmental

consequences of human activities. The way human activities are linked to each other and

affect different compartments of the biosphere has to be first understood (Vörösmarty et al.,

2000; Weisz and Lucht, 2009).

Climate change, for example, is currently seen as the most impending

environmental issue deterring societies from sustainability. Unfortunately, in the search for

sustainability, decision makers have approached sustainable development through the

climate change lens (Robinson et al., 2006), ignoring that the impact on the atmosphere is

just one aspect of the human-induced environmental impacts. Looking at carbon in isolation

– rather than a symptom of humanity’s overall metabolism of resources – has made us blind

to other dangers. The world’s appetite for water, food, timber, marine, and many other

resources is also relevant (Ewing et al., 2009; Fischer-Kowalski and Haberl, 2007; Giljum

et al., 2009b; Krausmann et al., 2009; WWF, 2008).

By bringing together ecological, carbon, and water footprints into a single

conceptual framework, this paper moves in the direction of an integrated ecosystem

approach. However, it is acknowledged that these three indicators, even when used together

as a “footprint family” of indicators, cannot provide a full sustainability assessment.

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7.1.3 The need for consumer approach

If we lived in a world where countries produced and consumed all goods and services

within the boundaries of their country, the distinction between consumption-based and

production-based accounting would be unnecessary. But we live in a highly globalized

world, where economies of scale and comparative advantage in certain areas exist,

rendering trade and commerce highly valuable and “responsibility” over impacts much

more complex. For instance, given the existing global environmental policy framework

(e.g. Kyoto protocol) holding producers rather than final consumers responsible for human

impact, a perverse incentive exists for industrialized countries to transfer high-impacting

activities to the developing world by importing environmentally-inefficient goods and

services, rather than achieve decoupling and changes in peoples’ consumption behaviour in

domestic economies (Galli et al., forthcoming). In recent years, consumption-based

accounting (CBA) has become increasingly relevant as it provides several opportunities for

policy and decision making processes (Wiedmann, 2009c) (e.g., quantifying trade

relationships among countries, understanding the common although distinct responsibilities

between producer and consumer countries, etc.).

The ecological, carbon, and water footprints emphasize the analysis of human

demand from a consumer rather than a producer perspective. These indicators are not based

on who produces a good or service but on the end-users that consume them. Due to their

consumption-based approaches, these indicators present a quantifiable and rational basis on

which to begin discussions and develop answers on the limits to resource consumption, the

international distribution of the world’s natural resources, and how to address the

sustainability of the use of our ecological assets across the globe (Senbel et al., 2003).

However, if consumption-based accounting is to be accepted and used by decision

makers, the tools to be used and their underlying calculation methodologies need to be

reliable, robust and tested against relevant criteria (Wiedmann, 2009c).


7.2 Methods

Three indicators have been selected to be included in the footprint family: ecological,

carbon and water footprint. Beyond the similarity in the name, these three methods have

been selected because of their aims and underlying research questions. Their different, yet

complementary, points of view allow for a more comprehensive tracking of the demand

humans place on the planet and its set of ecosystem compartments.

7.2.1 Ecological footprint

The ecological footprint is a resource and emission7 accounting tool designed to track

human demand on the biosphere’s regenerative capacity (Wackernagel et al., 1999a, 2002).

It documents both direct and indirect human demands for resource production and carbon

dioxide assimilation and compares them with the planet’s ecological assets (biocapacity)

(Monfreda et al., 2004; Wackernagel et al., 1999b).

By tracking a wide range of human activities, the ecological footprint monitors the

combined impact of anthropogenic pressures that are more typically evaluated

independently (carbon dioxide emissions, fisheries collapse, land degradation/ land-use

change, etc.) and can thus be used to understand, in an integrated manner, the

environmental consequences of the pressures humans place on the biosphere and its

composing ecosystems. The ecological footprint can be applied at scales ranging from

single products, to cities and regions, to countries and the world as a whole (Ewing et al.

2009).

Six key ecosystem services widely demanded by the human economy are tracked

and associated with a type of bioproductive land: plant-based food and fibre products

(cropland); animal-based food and other animal products (cropland and grazing land -

agricultural land); fish-based food products (fishing grounds); timber and other forest

7 CO2 is the only greenhouse gas accounted by the Ecological Footprint method.

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products (forest); absorption of fossil carbon dioxide emissions (carbon uptake land8); and

the provision of physical space for shelter and other infrastructure (built-up area).

Ecological footprint and biocapacity values are used to measure one main aspect

of sustainability only: the human appropriation of the Earth’s biological capacity. They

analyse the human predicament from this distinct angle, motivated by the assumption that

Earth’s regenerative capacity might be the limiting factor for the human economy if human

demand continues to overuse beyond what the biosphere can renew. In doing so, ecological

footprint and biocapacity accounting take into account both the sustainability principles

identified by Herman Daly (1990) as they identify the extent to which human activities

exceed a) the availability of bioproductive land to produce resources and b) the availability

of forests to uptake carbon dioxide emissions.

Both ecological footprint and biocapacity are resource flow measures. However,

rather than being expressed in tonnes per year, each flow is expressed in units of area,

annually necessary to provide (or regenerate) the respective resource flow. This reflects the

fact that many basic ecosystem services and ecological resources are provided by surfaces

where photosynthesis takes place. These surfaces are limited by physical and planetary

constraints and the use of an area better communicates the existence of physical limits to

the growth of human economies (GFN, 2010; Monfreda et al., 2004).

As bioproductivity differs between various land use types and countries,

ecological footprint and biocapacity values are usually expressed in units of world average

bioproductive area, namely global hectares – gha (Galli et al., 2007; Monfreda et al., 2004;

Wiedmann and Lenzen, 2007). Yield factors and equivalence factors are the two ‘scaling

factors’ used to express results in terms of global hectares (Galli et al., 2007; Monfreda et

al., 2004), thus allowing comparisons between various countries’ ecological footprint

and/or biocapacity.

8 It should be noted that the demand for the biosphere’s carbon uptake capacity is usually also

referred to as “carbon footprint”, though this should not be confused with the “Carbon Footprint”

methodology described in section 7.2.


7.2.2 Carbon footprint

The carbon footprint measures the total amount of GHG emissions that are directly and

indirectly caused by an activity or are accumulated over the life stages of a product9. This

includes activities of individuals, populations, governments, companies, organizations,

processes, industry sectors, etc. In any case, all direct (on-site, internal) and indirect

emissions (off-site, external, embodied, upstream, and downstream) need to be taken into

account. More specific aspects such as which GHGs are included and how double-counting

is addressed can vary (Wiedmann and Minx, 2008).

When applied to a country, the carbon footprint relates to consumption of goods

and services by households, governments, and other 'final demand' such as capital

investment. It also relates to the GHG emissions embodied in trade: the carbon footprint of

a country is the sum of all emissions related to a country's consumption, including imports,

but excluding exports. As such, the consumption-based perspective of the carbon footprint

complements the production-based accounting approach taken by national greenhouse gas

inventories, such as those considered by the Kyoto Protocol, which look at the emissions

occurring on the territory of the country. Such consumption-based approach could

encourage and facilitate international cooperation and partnerships between developing and

developed countries, for example by prioritizing technology transfers, estimating financial

transfers, and streamlining Clean Development Mechanisms (CDM). Moreover, from a

communication point of view, CBA can be used to make consumers aware of the GHG

emissions from their life-style and consumption choices. Likewise, CBA raises awareness

of indirect emissions in governments and businesses.

Despite its name, the carbon footprint is not expressed in terms of area. The total

amount of greenhouse gases is simply measured in mass units (kg, t, etc.) and no

conversion to an area unit (ha, m2, km2, etc.) takes place. Any conversion into a land area

would have to be based on a variety of assumptions that would increase the uncertainties

and errors associated with a particular carbon footprint estimate.

9 Products include goods and services.

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When only CO2 is included, the unit is kg CO2; if other GHGs are included the

unit is kg CO2-e, expressing the mass of CO2-equivalents. Those are calculated by

multiplying the actual mass of a gas with the global warming potential factor for this

particular gas, making the global warming effects of different GHGs comparable and

additive. In most cases, the six greenhouse gases identified by the Kyoto Protocol are

included in the analysis: CO2, CH4, N2O, HFC, PFC, and SF6.

7.2.3 Water footprint

The water footprint concept was introduced in response to the need for a consumption-

based indicator of freshwater use (Hoekstra, 2003). It accounts for the appropriation of

natural capital in terms of the water volumes required for human consumption (Hoekstra,

2009). The water footprint concept is closely linked to the virtual water concept (Allan,

1998): the volume of water required to produce a commodity or service.

The water footprint looks at both direct and indirect water use of a consumer or

producer. Three key water components are tracked in its calculation: the blue water

footprint refers to consumption of surface and ground water (blue water resources); the

green water footprint refers to consumption of rainwater stored in the soil as soil moisture

(green water resources); the grey water footprint refers to pollution and is defined as the

volume of freshwater required to assimilate the load of pollutants based on existing ambient

water quality standards (Hoekstra et. al, 2009).

Water footprint can be calculated for a particular product, for any well-defined

group of consumers (e.g. an individual, family, village, city, province, state, or nation) or

producers (e.g. a public organization, private enterprise, or economic sector) and it is

defined as the total volume of freshwater that is used to produce the goods and services

consumed by the individual or community or produced by the business (Hoekstra and

Chapagain 2008). The first assessment of water footprints of countries was carried out by

Hoekstra and Hung (2002). A more extended assessment was done by Chapagain and

Hoekstra (2004).


The water footprint concept aims primarily at illustrating the hidden links between

human consumption and water use and between global trade and water resources

management. This concept has been brought into water management science in order to

show the importance of human consumption and global dimensions in good water

governance (Hoekstra, 2009). The water footprint is a geographically explicit indicator, not

only showing volumes of water use and pollution, but also the locations.

Water use is measured through the water footprint method in terms of water

volumes consumed (evaporated or incorporated into the product) and polluted per unit of

time. Depending on the level of detail that one aims to provide, it can be expressed per day,

month, or year (Hoekstra et. al, 2009).

7.3 Discussion

7.3.1 Testing and comparing footprint indicators

According to van den Bergh’s and Verbruggen (1999), the search for operational indicators

should be guided by a number of specific criteria that indicators or set of indicators should

meet. This has been a guiding principle in analysing the ecological, carbon , and water

footprint, which have been tested against criteria such as scientific robustness, presence of a

clear research question, policy usefulness, temporal and spatial coverage, etc. Similarities

and differences among the three indicators have been also highlighted to show how the

indicators overlap, interact, and complement each other.

As any indicator is, by definition, a simplification and modelling of a much more

complex reality, sets of indicators such as the footprint family or alternative “baskets of

indicators” could be more informative for policy makers (e.g., Best et al., 2008); however,

their range of application as well as usefulness in tracking the functioning of a larger scope

of the Earth’s ecosystems has first to be tested before they can be actually adopted.

The outcomes of the indicators’ testing phase have been reported in Table 7.1

below. Information reported in this table has then been used as starting points in defining

the “footprint family” concept.

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All three indicators were found able to represent the environmental consequences

of human activities and complementary in assessing human pressure on the planet from a

consumer-based angle; however, they are built upon different research questions and tell

different stories.

The ecological footprint focuses on the aggregate demand that resource

consumption places on the planet’s ecological assets; thus recognizing the existence of

limits to our growth and trying to measure them. The water footprint focuses on the human

appropriation of natural capital in terms of fresh water volumes required for human

consumption; it is primarily intended to illustrate the hidden links between consumption

activities and water use. The carbon footprint focuses on the total amount of GHGs released

due to resource-consumption activities; by complementing the production-based accounting

approach taken by national GHG inventories, the carbon footprint provides a better

understanding of humans’ contribution to GHG emissions.

All three indicators are characterized by a wide spatial coverage and scale of

applicability: they can all be applied to single products, cities, regions, nations and up to the

whole planet. In terms of time coverage, the ecological footprint was found to be the most

comprehensive as it covers a 1961-2006 time period, while values exist for the year 2001

and an averaged 1996-2005 period only for the carbon and water footprint, respectively.

The three indicators are all able to track both direct and indirect human demands,

thus favouring a clear understanding of the ‘hidden/invisible’ human-induced sources of

pressure. However, only the ecological and water footprint were found to be able to account

for both the source (resource production) and sink (waste assimilation) capacity of the

planet. The ecological footprint was then found to be the sole indicator able to provide a

clear ecological benchmark (biocapacity) to test human pressure against. Setting a

benchmark for the carbon footprint indicator is currently being considered in the OPEN:EU

project (OPEN:EU, 2010).


Table 7.1. Footprint family - summary table . Ecological footprint Carbon footprint Water footprint

Research question

The amount of the biosphere’s regenerative capacity that is directly and indirectly (i.e. embodied in trade) used by humans (namely ecological footprint) compared with how much is available (namely biocapacity), at both local and global scale.

The total amount of GHG emissions (CO2, CH4, N2O, HFC, PFC, and SF6) that are directly and indirectly caused by human activities or accumulated over the life stages of products.

The human appropriation of the volume of freshwater required to support human consumption.

Main message

To promote recognition of ecological limits and safeguard the ecosystems’ life-supporting services enabling the biosphere to support mankind in the long term.

The consumption-based perspective of the carbon footprint complements the production-based accounting approach taken by national GHG inventories (e.g., those considered by the Kyoto Protocol).

The water footprint concept is primarily intended to illustrate the hidden links between human consumption and water use and between global trade and water resources management.

Data and sources

• Data on local production, import and export for agricultural, forestry and fisheries products (FAOSTAT, UN Comtrade);

• Land use data (FAOSTAT, etc.);

• Local and trade-embedded CO2 emissions (IEA and others); and

• Land yield (FAOSTAT) and potential crop productivity (provided by the FAO GAEZ model) – this data is needed to express results in units of global hectares.

• National economic accounts (supply, use, input-output tables);

• International trade statistics (UN, OECD, GTAP and others); and

• Environmental accounts data on GHG emissions (IEA, GTAP, and others).

• Data on population (World Bank);

• Data on arable lands (FAO) and total renewable water resources and water withdrawals (FAO);

• Data on international trade in agricultural (PC-TAS) and industrial products (WTO); and

• Local data on various parameters such as climate, cropping patterns, soil, irrigation, leaching, water quality, pesticides and fertilizers rates, etc.

Unit of measurement

• Global hectares (gha) of bioproductive land. Gha is not a measure of area but rather of the ecological production associated with an area; and

• Results can also be expressed in actual physical hectares.

• Kg CO2 when only CO2 is included or kg CO2-e when other GHGs are included as well; and

• No conversion to an area unit takes place to avoid assumptions and uncertainties.

• Water volume per unit of time (usually m3/yr.) for the water footprint of processes;

• m3/ton or litre/kg for the water footprint of products; and

• Water volume per unit of time for the water footprint of a geographical area.

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Indicator coverage

• Temporally explicit and multi-dimensional indicator that can be applied to single products, cities, regions, nations and the whole biosphere;

• More than 200 countries for the period 1961-2006 are tracked (Ewing et al., 2009a);

• Documents both direct and indirect human demands for both the source (resource production) and the sink (carbon uptake) capacity of the biosphere;

• Provides a measure of both human demand and nature supply;

• Provides a clear benchmark; and

• It has a consumption-based point of view and thus considers trade.

• Multi-dimensional indicator that can be applied to products, processes, companies, industry sectors, individuals, governments, populations, etc.;

• 73 countries and 14 regions for the year 2001 only are tracked (Hertwich and Peters, 2009);

• Documents all direct and indirect GHGs emissions due to use of resources and products (source);

• Measures the ‘demand’ side only, in terms of the amount of GHGs emitted; and

• It has a consumption-based point of view and thus considers trade.

• Geographically explicit and multi-dimensional indicator: it can be calculated for products, public organizations, economic sectors, individuals, cities and up to countries;

• High-spatial resolution (0.5’) 1996-2005 are tracked (Hoekstra and Mekonnen 2012);

• Documents both the direct and indirect use of natural capital as a source (demand on blue and green waters) and as a sink (grey water to dilute pollution);

• Measures the ‘demand’ side only, in terms of freshwater consumed (by sources) and polluted (by type of pollution) by human activities;

• No benchmark is provided; and

• It has a consumption and production -based approach and considers trade.

Strengths

• Allows benchmarking human demand for renewable resources and carbon uptake capacity with nature supply and determining clear targets.

• Provides an aggregated assessment of multiple anthropogenic pressures; and

• Easy to communicate and understand with a strong conservation message.

• Allows for a comprehensive assessment of human contribution to GHG emissions; and

• Consistent with standards of economic and environmental accounting.

• Consistent emissions data available for the majority of countries.

• Represents the spatial distribution of a country’s water “demand”;

• Expands traditional measures of water withdrawal (green and grey waters also included); and

• Visualizes the link between (local) consumption and (global) appropriation of freshwater. Integrates water use and pollution over the production chain.


Weaknesses

• Cannot cover all aspects of sustainability, neither all environmental concerns, especially those for which no regenerative capacity exists (including abiotic resources);

• Shows pressures that could lead to degradation of natural capital (e.g. reduced quality of land or reduced biodiversity), but does not predict this degradation; and

• Not geographically explicit. • Some underlying

assumptions are controversial, though documented

• Cannot track the full palette of human demands on the environment

• Additional impact assessment models are needed to analyse the impact of climate change at both national and sub-national levels;

• Efforts needed to set up and update a system of MRIO tables and related environmental extensions; and

• No benchmark is provided.

• Only tracks human demand on freshwater;

• It relies on local data frequently unavailable and/or hard to collect. It suffers from possible truncation errors;

• No uncertainty studies are available, though uncertainty can be significant; and

• Grey water calculation heavily relies on assumptions and estimations.

Policy usefulness

• Measures ‘overshoot’ and identifies the pressures that humanity is placing to various ecosystem services;

• Monitors societies’ progresses towards minimum sustainability criteria (demand ≤ supply);

• Monitor the effectiveness of established resource use and resource efficiency policies;

• Allows analysing the consequences of using alternative energies;

• Communicate environmental impacts of different life-styles to the overall public;

• Track pressure on biodiversity; and

• Illustrates the unequal distribution of resource use and can be used to design international policies aiming at implementing contraction and convergence principles.

• Offers an alternative angle for international policy on climate change as it complements the territorial-based approach used by the UNFCCC;

• Offers a better understanding of countries’ responsibility and could facilitate international cooperation and partnerships between developing and developed countries;

• Can help design an international harmonized price for greenhouse gas emissions; and

• Illustrates the unequal distribution of resource use and can be used to design international policies aiming at implementing contraction and convergence principles.

• Gives a new & global dimension to the concept of water management & governance;

• Offers nations a better understanding of their dependency on foreign water resources;

• Offers river basin authorities info on the extent to which scarce water resources are allocated to low-value export crops;

• Offers companies a way to monitor their dependence on scarce water resources alongside their supply-chain; and

• Illustrates the unequal distribution of resource use and can be used to design international policies aiming at implementing contraction and convergence principles

Complementary properties in the footprint family

• Uses a consumer-based approach to track human pressures on the planet in terms of the aggregate demand that resource-consumption and CO2 emissions places on the ecological assets.

• Uses a consumer-based approach to track human pressures on the planet in terms of total GHG emissions and human contribution to climate change.

• Uses a consumer-based approach to track human pressures on the planet in terms of the water volumes required for human consumption.

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All three indicators illustrate the unequal distribution of resource use and/or

related impacts between the inhabitants of different world regions and could thus be linked

to policy debates in the development policy area, oriented at concepts such as “Contraction

and Convergence,” “Environmental Justice,” or “Fair Share”.

A partial overlap exists between ecological and carbon footprint as human-induced

CO2 emissions are tracked by both methodologies. However, both methodologies go

beyond the sole CO2 investigation as the carbon footprint also tracks the release of

additional GHGs (usually CO2, CH4, N2O, HFC, PFC, and SF6) and the ecological footprint

expands its area of investigation by looking at human demand for food, fibres, wood

products, etc.

For what concern ecological and water footprint, a partial overlap between these

two indicators was found as water is tracked by both methodologies. But while direct and

indirect freshwater requirements are clearly tracked by the water footprint indicator, the

water issue is only indirectly tracked by the ecological footprint, which is able to provide

limited information to back up water policies. As recognized by Kitzes et al. (2009),

freshwater is a natural resource cycling through the biosphere, whose availability or

scarcity influence the regenerative capacity (biocapacity) of the planet; however, water is

not itself a creation of the biosphere for which the planet has a regenerative capacity. As

such the direct ecological footprint of a given quantity of water cannot be calculated,

though it is possible to measure the ecological footprint embedded in the provisioning of

water (Lenzen et al., 2003). The combined use of ecological and water footprint within the

footprint family suite of indicators is thus deemed to be the best approach to develop a

multi-criteria decision making process and arrive at optimal decisions.

7.3.2 Definition of the footprint family

The combination of indicators presented in this research is not the first attempt at a

combined footprint approach for the assessment of the environmental impact of productions

(Giljum et al., 2009c; Niccolucci et al., 2010; Patrizi, 2009). However, the OPEN:EU


project is to our knowledge the first attempt at clearly providing a definition to the footprint

family of indicators in its wider range of applicability.

By looking at the amount of bioproductive area people demand because of

resource consumption and waste emission, the ecological footprint can be used to inform on

the impact placed on the biosphere. By quantifying the effect of resource use on climate,

the carbon footprint informs on the impact humanity places on the atmosphere. Lastly, by

tracking real and hidden water flows, water footprint can be used to inform on the impact

humans place on the hydrosphere.

The footprint family is thus here defined as a set of indicators - characterized by a

consumption-based perspective - able to track human pressure on the surrounding

environment, where pressure is defined as appropriation of biological natural resources and

CO2 uptake, emission of GHGs, and consumption and pollution of global freshwater

resources. Three key ecosystem compartments are monitored, namely the biosphere,

atmosphere, and hydrosphere through the ecological, carbon , and water footprint,

respectively.

The footprint family has a wide range of research and policy applications as it can

be employed at scales ranging from a single product, a process, a sector, up to individual,

cities, nations and the whole world.

The footprint family provides an answer to three specific research questions and

helps to more comprehensively monitor the environmental pillar of sustainability.

However, it is not yet a full measure of sustainability as several environmental (e.g.,

toxicity, soil quality and land degradation, nuclear wastes, etc.), economic, and social issues

are not tracked.

7.3.3 The need for a streamlined ecological-economic modelling framework

Although grouped for the first time under a single conceptual framework – the footprint

family - each of the three indicators is currently characterized by its own calculation

methodology and accounting framework as reported in the scientific literature: carbon

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footprint accounts (Hertwich and Peters, 2009) utilize a Multi-Regional Input-Output

(MRIO) model to allocate emissions to consumption; conversely ecological and water

footprint have been historically calculated using process-based LCA data and physical

quantities of traded goods (Hoekstra et al., 2009; Kitzes et al., 2008b).

In defining the footprint family concept, a need was identified to bring ecological,

carbon , and water footprint together under a single streamlined ecological-economic

modelling framework if the three indicators are to be used jointly as a suite of indicators.

For instance, integrating ecological, carbon , and water footprint accounts with an MRIO

modelling framework would strengthen the robustness and consistency of the footprint

family concept as this would enable an inter-industry analysis of the linkages across

multiple economies. Assessing trade-off would also be easier.

Currently, the Norwegian Institute of Science and Technology, Global Footprint

Network, and University of Twente are working to develop such streamlined modelling

framework, which will be then tested and explored for use in the OPEN:EU project (Ewing

et al., 2012). In building such model, efforts are being made to go beyond the “classical”

input-output/footprint approaches that have been proposed in the past. Particularly, a multi-

regional input-output model will be used and a high level of detail in commodity

classification ensured while integrating existing accounts for ecological and water

footprints within a more complete (but less detailed) MRIO framework: direct footprint

requirements will be calculated with a process-based type of approach and indirect footprint

requirements via a monetary model (Ewing et al., 2012).

However, such integration process conveys both pros and cons. While the

integration of environmental and economic accounts is extremely valuable, approximations

are required as part of the calculation to utilize economic flows as a proxy for the physical

flows. Moreover, the use of Input-Output tables with footprint indicators causes a decreased

time coverage (as MRIO models usually refer to a single year only) and resolution (because

of the shift from detailed product-level to aggregated sectoral-level assessments). The

benefit of a purely physical flow accounting approach—where economic data is not

introduced into the models—is that the product resolution is much higher and these


accounts track the physical flows directly. However, the weakness of this approach is that

physical flow datasets are less prevalent and developed than the economic flows related to

the same products and the physical flow data sets only track goods, excluding services.

These physical flow accounts also do not completely link with the supply chain or the

economic activities that are driving the resource or waste flows (Ewing et al., 2012).

7.4 The role of the footprint family in the EU policy context

7.4.1 Resource use trends at EU level

The European Union uses 20% of what the world's ecosystems provide in terms of fibres,

food, energy, and waste absorption (WWF, 2005). Home to only 7% of the world

population, Europe's demand on the biological capacities of the planet has risen by more

than 70% since 1961 (Ewing et al., 2009; WWF, 2008).

Inhabitants of Europe have per capita resource consumption levels around 3 to 5

times higher than those of developing countries (Giljum et al., 2009a; WWF et al., 2008).

While extraction of natural resources has stabilized in Europe over the past 20 years,

imports of raw materials and products have significantly increased (Dittrich, 2009; Weisz et

al., 2006). Residents of the global South thus continue to bear the negative impacts of EU

profligate consumption: a) these negative impacts stem from extraction and processing of

raw materials (such as metal ores, timber, agricultural products, etc.) as the material basis

for products consumed in Europe; b) the global South bears an over proportional burden

from waste and emissions originating from European consumption. For example, each EU-

27 citizen emitted on average 10.2 t of GHG emissions (in CO2-equivalents) in 2009 (EEA,

2009). This number has been falling in the past years due to efforts to decrease domestic

emissions, however, GHG emissions embodied in European imports from other world

regions have risen rapidly in the past 15 years (Bruckner et al. 2010; Peters and Hertwich,

2008; Wiedmann et al., 2008). Europe is also expanding built-up and urban areas for

housing, industrial, and commercial sites and transport networks. From 1990 to 2000, more

than 4.000 km² of agricultural and pasture land were transformed into built-up land,

increasing anthropogenic pressures on biodiversity (EEA, 2006).

227

Fresh water is increasingly becoming a global resource, driven by growing

international trade in goods and services. Europe, particularly Western Europe, is one of the

largest virtual water importers in the world with an import of 152 Gm3 year-1 (Chapagain

and Hoekstra, 2004); the people of Europe have higher water footprints per capita than the

world average (especially in south European countries such as Greece, Italy and Spain,

2300-2400 m3 year-1 per capita). Additionally, European counties are more dependent on

foreign water resources for their consumption activities. In some European countries

external water footprints contribute to 50-80% of the total water footprint (e.g. Italy,

Germany, the UK, and the Netherlands) (Chapagain and Hoekstra, 2004).

A shift to a more sustainable future, therefore, requires a qualitative and

quantitative understanding of the drivers in play, as well as a significant mobilization and

behavioural change of actors and institutions from all sides of the public, private, and

consumer spheres. A sustainable future for Europe can be achieved only by building an

economy that respects environmental limits (including biodiversity) while also improving

social and financial health.

7.4.2 The EU policy context

Acknowledging the need to understand and account for the main drivers behind Europe’s

use of natural resources and related environmental impacts, the European Commission (EC)

launched several strategies calling for such assessments including, among others, the

Sustainable Development Strategy (SDS), and the Thematic Strategy on the Sustainable

Use of Natural Resources.

However, despite widespread support in different EU policy fields for the general

ideas of increasing resource efficiency and reducing negative environmental impacts, little

concrete action has been taken. No quantitative targets have been formulated for increased

resource productivity or for a reduction of environmental impact of resource use in any of

the main EU policies. Most resource policy documents remain on a general level of

declarations of intent, without detailing those concrete policy measures that should be


implemented to achieve the formulated objectives. A strategy to systematically adjust EU

policies to promote resource productivity in the EU is far from being realised.

The One Planet Economy Network: Europe (OPEN:EU) project originates from

the willingness to answer the renewed EU Sustainable Development Strategy (SDS) call for

the development of indicators able to capture the full complexity of Sustainable

Development. To this end, the concept of footprint family has been introduced and its

potential policy usefulness explored and reported in the section below.

7.4.3 Policy usefulness of the footprint family

The review of the three footprint indicators has helped to clearly define each indicator’s

specific-although-limited research question. It has also highlighted that taken alone each of

these indicators reflects only one part of the whole “sustainability” picture and cannot be

used as a stand-alone indicator. For instance, although the issues tracked by the ecological

footprint are clearly relevant to sustainability, the messages that can be derived from

ecological footprint accounting may not provide the full information relevant for EU policy

goals (e.g. no information is provided on health issues or the consumption of non-

renewables resources). The ecological footprint alone does not reveal all important facts

about environmental impacts and the same is true for the water and carbon footprints.

Because of its limited scope (human appropriation of freshwater resources), the

water footprint alone is in fact not sufficient to inform policy makers and consumers about

the (un)sustainability of human natural resource use, and could benefit from being

integrated with other indicators. The carbon footprint is designed to calculate all direct and

indirect GHG emissions from all types of sources and it can be considered complete for

such GHG accounting. However, the range of human-induced pressures on the environment

is much broader that just GHG emissions and thus, if used in isolation, the carbon footprint

is not adequate to address issues related to sustainability and anthropogenic pressures.

Conversely, the joint use of ecological, carbon , and water footprint as a suite of

indicators (footprint family) provides a better overarching picture of the human pressure on

the natural environment and its key compartments (namely biosphere, atmosphere, and

229

hydrosphere), where indicators compensate for each other flaws and complement each

other in assessing trade-offs as well as real pressure reductions rather than just pressure

shifting from one compartment to the others.

A small suite of indicators is deemed to be the best approach for measuring the

overall environmental impacts of production and consumption. Although not all aspects are

included when these three indicators are used (e.g. no indication of ecosystem disturbance,

land use quality and/or impact on biodiversity, no tracking of the depletion of non-

renewable resource stocks, no information on people health and well-being, no information

on air quality), the use of the footprint family still provides information on sustainable

development to a satisfactory extent (Knoblauch and Neubauer, 2010).

Here the footprint family has been tested against some of the main European (and

international) policy objectives in an attempt to identify which indicator can best address

the specific environmental issues EU policy makers have to face, as well as the value added

of addressing such issues with the whole footprint family suite of indicators. Outcomes of

this “policy-testing” have been summarized in Figure 7.1; however, it has to be noted that

only the most relevant policy and policy fields have been considered in this analysis.

Concerning the EU Sustainable Development Strategy (SDS), the ecological,

carbon , and water footprint are partly suitable to inform policy makers as they contain

information relevant for strategy but do not cover all the policy fields covered by the

strategy. Although extended in the range of applicability, even the footprint family is not

able to inform on all the different aspects of the EU SDS. In particular, out of the seven key

challenges included in the EU SDS, only three (climate change and clean energy;

sustainable production and consumption; conservation and management of natural

resources) can be informed by the footprint family, while the other four (sustainable

transport; public health; social inclusion, demography and migration; and global poverty

and sustainable development challenges) are not covered. The footprint family is thus only

partially suitable to inform policy makers on EU SDS.


The same is true for the EU 6th Environmental Action Plan (6EAP). Some of the

aspects are covered through the ecological, carbon , and water footprints but none of the

indicators covers all aspects. In particular, the following four key priority areas are relevant

for the 6EAP: climate change; nature and biodiversity; environment, health and quality of

life; natural resources and wastes. The ecological footprint is suitable to inform on nature

and biodiversity, natural resources and wastes, and only partly on environment, health and

quality of life. The carbon footprint is at least partly suitable to inform about climate

change (it only directly informs about GHG emissions and not climate change). The water

footprint is suitable to partly inform about natural resources and waste since water is also a

natural resource. The aspect of health and quality of life is covered by none of the

indicators.

Figure 7.1. Indicator-Policy Radar. It summarizes the range of applicability and the depth of

the assessment for each of the footprint indicators as well as the whole footprint family. For

any given policy, the radar highlights whether the indicator is able to address such policy

fully (100), sufficiently (75), partially (50), marginally (25) or not at all (0).

Ecological Footprint Carbon Footprint

Water Footprint Footprint Family

Ecological Footprint Carbon Footprint

Water Footprint Footprint Family

231

Four of the seven Thematic Strategies (TS) within the 6EAP can be partly

informed by the footprint family: the carbon footprint partly informs on the Thematic

Strategy on air pollution (TS Air), the water footprint partly informs on the Thematic

Strategy on the marine environment (TS Marine), and the ecological footprint can partly

inform the Thematic Strategy on the prevention and recycling of waste (at least indirectly

through addressing the overexploitation) (TS Waste). The Thematic Strategy on the

sustainable use of natural resources (TS Resources) can be fully informed by the ecological

footprint and as regards water by the water footprint. The remaining three thematic

strategies (urban environment, sustainable use of pesticides and soil protection) cannot be

informed by the single footprint indicators neither by the footprint family.

The 2005 Lisbon Strategy primarily focused on social and economic aspects and

thus none of the footprint Indicators is suitable to inform policy makers for this strategy.

Conversely, the Europe 2020 Strategy includes environmental and climate targets and thus

the ecological, water, and carbon footprint are partly suitable to inform on the headline

target of the renewed strategy (e.g., carbon footprint informs on the headline target to

reduce the GHG emissions and the ecological and water footprint inform on the flagship

initiative on a “Resource Efficient Europe”). However, most of the headline targets and

flagship initiatives focus on issues that cannot be informed by the footprint family (e.g.

employment rates, share of early school leavers, poverty, youth, internet, etc.).

The Directive on renewable energy (Directive 2009/28/EC), the Forestry Strategy

as well as the Forest Action Plan are all resource related policies and the ecological

footprint was found to be informative to address them (Knoblauch and Neubauer, 2010).

However, since the indicator is an aggregated one, it can only help decision makers grasp

the big picture and understand the links between such policies but it may not be suitable to

inform policy makers concerning a specific resource (e.g. forests).

The various water use policies - especially those addressing water scarcity and

resource productivity - can partly be informed by the water footprint (e.g., Water

Framework Directive - WFD). However, to derive conclusions for practical policies from

the number provided by the water footprint, one would need to compare the existing water


resources in the considered country with the water use numbers provided by the water

footprint. As regards water pollution, specifically the grey water footprint informs on the

water quantity that would be needed to dilute water polluted for the use of production or

providing services to neutralize the pollution. The water footprint was found to be not

informative regarding the Drinking Water Directive (DWD).

The Common Fisheries Policy (CFP) defines the type and amount of fish that each

Member State is allowed to catch. Since the ecological footprint includes the fishing ground

biocapacity and footprint in its calculation, it is suitable to addresses the CFP. However,

due to its aggregate nature, it can help decision makers grasp the big picture but it may not

be suitable to inform policy makers concerning a specific resource (e.g. fish stocks) as it

does not deal directly with policy-responsive issues. Moreover, the current fishing grounds

ecological footprint and biocapacity trends are not able to show fish stock depletion;

additional research is needed (Ewing et al., 2009; Kitzes et al., 2009) and ongoing

(Hartman et al., 2010) to improve such calculation.

The climate related policies reported in Figure 7.1 can all be partly informed by

the carbon footprint as it measures emissions for six main GHGs; still carbon footprint

values need to be interpreted in context (i.e. their reduction needs to be analysed in a time

series) to derive information on climate change. It has been suggested that the most serious

consequences of global warming might be avoided if global average temperatures rose by

no more than 2 °C above pre-industrial levels. Recent research suggests that it would be

necessary to achieve stabilization below 400 ppm of carbon dioxide in the atmosphere to

give a relatively high certainty of not exceeding 2 °C and a concentration of 350 ppm

carbon dioxide has been advocated as an appropriate level. As of April 2010, carbon

dioxide concentration in the Earth's atmosphere was 391 ppm by volume; this renders any

additional emissions as 'unsustainable' and the carbon footprint informative to cover the

issue.

Regarding the Convention on Biological Diversity (CBD), the ecological footprint

has been officially included in the list of indicator that the Biodiversity Indicator

Partnership (BIP) is using to monitor world governments progress toward the 2010

233

biodiversity target set by the CBD in 2002. The BIP approaches biodiversity with a

Pressure-State-Benefit-Response framework and the ecological footprint is one of the

pressure indicators used (Butchart et al., 2010). The ecological footprint is thus related to

the biodiversity issue in that it is a measure of the human pressure on ecosystems and

biodiversity; time series ecological footprint assessments constitute a way to measure how

this pressure has changed over time. The carbon and water footprint were found not

informative for CBD.

All of the footprint Indicators as well as the footprint family are partly suited to

inform policy makers on the UN Millennium Development Goals (MDGs). Particularly,

Goal 7 (Ensure Environmental Sustainability) refers to resource use/deforestation, climate

change and drinking water – all of which are issues that can be informed by the three

indicators. Because of its ability to analyse the extent of the global ecological assets each

country is using compared to what is available, the ecological footprint can inform on

issues such as equity in resource accessibility and use; this, in turn, can be used to partially

inform on Goal 8 (Develop a global partnership for development). However, since MDGs

are quite broad in their scope, the indicators are not suited to fully inform policy makers on

all issues addressed therein. All other MDGs cannot be informed by the footprint family.

The International Panel for Sustainable Resource Management (IPSRM) observes,

among others, exploitation of resources. The ecological footprint is thus fully suitable to

inform stakeholders concerned with the panel, while the water footprint informs about the

issues related to water use and productivity. By contrast, the carbon footprint is not dealing

with this topic and, consequently, cannot inform stakeholders on the issue.

The Marrakech Process deals with sustainable consumption and production issues.

The ecological, water, and carbon footprint are also dealing with consumption and

production issues: the first concerning the bioproductive land appropriation, the second

concerning water use, and the third regarding the emissions perspective and they can

therefore inform the process. Moreover, with the development and implementation of a

streamlined MRIO-footprint model (Ewing et al., 2012), the footprint family will better


inform these processes via linking the information on consumption and production, i.e.

traces the footprint along the full supply chain.

Since the adoption of the Health Check, new challenges have been highlighted for

the future Common Agricultural Policy (CAP), which brought environmental issues in a

stronger focus of European agriculture: climate change, the need for better water

management, the protection of biodiversity, and the production of green energy. Activities

and measures resulting from these challenges and further debated within the currently

ongoing CAP reform process can fully be informed by the ecological, carbon , and water

footprint as well as the whole footprint family.

The Directive on the conservation of natural habitats and of wild fauna and flora

(Directive 92/43/EEC) - Habitats Directive - aims to protect different land and water

habitats and species. This directive can be marginally - and only indirectly - informed by

the ecological and water footprint as these indicators show the aggregate pressure humans

place on various ecosystems and habitats. The carbon footprint was found not informative

for the habitats directive.

Last but not least, the Directive on the conservation of wild birds (Directive

2009/147/EC) - Birds Directive - is about establishing protected areas for birds thus

focusing on land protected for them. The policy can consequently be marginally - and only

indirectly - informed by the ecological footprint – as a measure of growing human pressure

- but not by the other indicators.

7.5 Conclusion

The footprint family of indicators introduced in this study is intended to assist policy

makers as well as academics, CSOs, and other practitioners in understanding the many

diverse pressures human activities place on the planet. It represents a quantifiable and

rational basis on which to begin discussions and develop answers on the limits to natural

resource and freshwater consumption, greenhouse gas emissions, as well as on how to

address the sustainability of natural capital use across the globe.

235

The need for developing such a family of indicators originates from the

understanding that, when used in isolation, each of the indicators considered in this paper is

able to capture just one limited aspect of the full complexity of sustainable development. As

a result, there is a lack in the indicators realm of methods and tools with which to fully

illustrate the links between economic growth and environmental degradation to policy

makers, CSOs and the public.

The footprint family proposed here can thus be used to improve researchers’

ability to track the current resource use and the impact this use generates, highlight the

main drivers of resource use (therefore providing information on the areas where actions

are needed), suggest solutions, and quantify the outcomes of specific policies undertaken to

reduce the negative environmental impacts of natural resource use. However, relevant

sustainability-related topics including human health and well-being still cannot be tracked

with the footprint family.

The three indicators selected are all characterized by the capacity to represent the

environmental consequences of human activities, though they are built around different

research questions and tell different stories. The ecological, carbon , and water footprint

have to be regarded as complementary in the sustainability debate and the footprint family

as a tool able to track human pressures on various life-supporting compartments of the

Earth (biosphere, atmosphere, and hydrosphere). The use of the footprint family of

indicators thus eases a multidisciplinary sustainability assessment and it also emphasizes

the strengths and dissipates the weaknesses of each indicator.

If Europe, or any other region, is to truly address sustainable development then

decision makers need multiple tools and sets of indicators. In reducing resource

consumption while improving economic well-being, all compartments (biosphere,

atmosphere, and hydrosphere) need to be taken into account and trade-offs understood to

avoid additional cost, or worse, inadvertently undoing progress in one sector by not

accounting for direct and indirect implications of actions in another sector.


Of the three indicators, the ecological footprint was found to have the widest

spectrum of applicability, though only the Thematic Strategy on the sustainable use of

natural resources and the International Panel for Sustainable Resource Management were

found to be fully and directly addressed by this indicator. Conversely, the carbon footprint

was found directly able to address the EU Climate Objectives, though its range of

applicability was found to be very narrow. The water footprint was found to be sufficiently

informative for the EU water policies. As a consequence, the footprint family was found to

be suitable for tracking human pressure on the planet and informative for policy and

decision makers. Although not yet comprehensive and unable to track some relevant

economic and social issues, the footprint family was found to cover a wide-enough

spectrum of policies and it is believed to provide information on sustainable development to

a satisfactory extent.

8 Discussions and conclusion

The motivation of this thesis has been to explore the application of the water footprint

methodology within the context of governmental policy and corporate strategy. Two

perspectives are addressed under one framework because solutions to freshwater scarcity

and pollution require not only better and effective governmental policy but also responsible

corporate engagement. Because of this connection, water footprint assessment can be an

effective tool in facilitating communication between governments and businesses in

response to shared concern over unsustainable water use.

Chapters 2 to 5 of this thesis aimed to illustrate how water footprint assessment

can yield meaningful information to governments and businesses that can feed and facilitate

discussion about sustainable water use. With the case studies in Chapters 2 and 3, we

showed how companies can employ the water footprint to assess their operational and

supply-chain water consumption and pollution including the relevant spatial and temporal

dimensions. Our analysis showed that a detailed supply-chain assessment should be

included in business water accounting as the bulk amount of water used by businesses is in

their supply-chain. This is particularly important as common practice in business water

accounting is mostly restricted to the analysis of operational water use and consequently

reduction targets are formulated with regard to operational water use. Understanding the

pressures that they put on water resources can help companies to mitigate the negative

impacts of their water use and the risks imposed on the business. This study revealed that

the knowledge about blue, green and grey components of the water footprint and the precise

locations and timing of water use is essential for formulating mitigation policies. This thesis

showed that water footprint assessment provides a comprehensive tool for businesses to

measure their water use and impacts which can help them identify risk, drive improvement

and sustain their businesses.

Linking specific consumer products in a country to water problems elsewhere is

still uncommon in governmental thinking about water policy. With Chapter 4, a case study

from governmental perspective, we visualized this link. The analysis presented in this

chapter included both water footprint accounting and sustainability analysis from both the

238/ Chapter 8. Discussion and conclusion

perspective of national production and the perspective of consumption. One of the

conclusions of this study is that analysing the water footprint of consumption is an

important addition to looking at the water footprint of production, because imported

consumer products can substantially contribute to water scarcity and pollution in

watersheds outside the country. Understanding the impacts in these watersheds is necessary

to understand the true sustainability national consumption. It is essential to look at how

much water is used and when it is used in order to assess the impacts on local ecosystems.

The analysis in this chapter showed that water footprint assessment can give valuable

information on how water resources of a nation are used, its dependency on water resources

of other nations and impacts of its consumption behaviour outside its borders.

Another application of the water footprint is presented in Chapter 5 on water

footprint scenarios, to show how consumer choices and other parameters will affect the

level of water consumption and pollution globally. This gives important information to

governments that can support the formulation of corrective policies at both national and

international levels, and can help to set priorities for the years ahead in order to achieve

sustainable and equitable use of the world’s water resources.

The reduction of humanity's water footprint depends on the combination of what

governments, businesses and consumers will do and how their different policies will

reinforce (or counteract) one another. This is addressed in Chapter 6 which compares

carbon and water footprints and shows how lessons from carbon footprint can be drawn to

benefit how policies are developed for the water footprint. Experience with the carbon

footprint suggests that for the development of comprehensive policy responses for water

footprint reduction, strong governmental leadership and action will be required.

Commitment and regulation are required at the national and international level.

Engagement of business through production systems and individuals through consumer

behaviour are also essential elements of effective change.

The water footprint by itself captures just one aspect of the full complexity of

sustainable development. This is addressed in Chapter 7 in which a ‘footprint family’ of

indicators is presented. The analysis concludes that no single indicator is able to

239

comprehensively monitor human pressure on the environment, but indicators rather need to

be used and interpreted jointly. Therefore, policy makers should interpret the information

provided by water footprint assessment carefully and consider the connections and trade-

offs between all aspects of sustainability in their decisions.

Future outlook

This thesis contributes to a better understanding of the role of governments and businesses

in managing humanity’s water footprint. However, it is limited to practical aspects of how

to apply the methodology of water footprint accounting and sustainability assessment. It

does not examine alternatives for policy responses that can be applied in practice.

Extending this study to the elaboration of specific types of impacts related to the water

footprint and to the examination and evaluation of various sorts of response measures by

governments and companies is recommended for future research. How to set quantitative

water-footprint reduction targets and benchmarking for businesses can be logical next steps.

The thesis considered sustainability of production and consumption from a water

resources perspective. Other environmental concerns need to be added for achieving a more

comprehensive understanding of sustainability. Besides, the sustainability analysis in this

thesis focuses on the environmental aspects of sustainability, leaving out social, economic

and institutional aspects. This is to be added in further research. Another line of new

research can aim to understand the role of other stakeholders in water governance, like

consumers, local communities and civil society organizations.

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List of publications

Papers in peer-reviewed journals

• Ercin, A.E., Aldaya, M.M. and Hoekstra, A.Y. (2011) Corporate water footprint

accounting and impact assessment: The case of the water footprint of a sugar-

containing carbonated beverage, Water Resources Management, 25(2): 721-741.

• Ercin, A.E., Aldaya, M.M. and Hoekstra, A.Y. (2012) The water footprint of soy milk

and soy burger and equivalent animal products, Ecological Indicators, 18: 392–402.

• Ercin, A.E., Mekonnen, M.M. and Hoekstra, A.Y. (2012) Sustainability of national

consumption from a water resources perspective: A case study for France, Ecological

Economics, in press.

• Ercin, A.E., and Hoekstra, A.Y. (submitted) Understanding carbon and water

footprints: Similarities and contrasts in concept, method and policy response,

Ecological Indicators.

• Ercin, A.E., and Hoekstra, A.Y. (submitted) Water footprint scenarios for 2050 - I: A

global analysis, Global Environmental Change.

• Ercin, A.E., and Hoekstra, A.Y. (submitted) Water footprint scenarios for 2050 - II:

case study for Europe, Global Environmental Change.

• Ewing, B. R., Hawkins, T. R., Wiedmann, T. O., Galli, A., Ercin, A.E., Weinzettel, J.,

and Steen-Olsen, K. (2012) Integrating ecological and water footprint accounting in a

multi-regional input–output framework, Ecological Indicators, 23(0), 1-8.

• Galli, A., Weinzettel, J., Cranston G. and Ercin, A.E. (2012a), A footprint family

extended MRIO model to support Europe's transition to a One Planet Economy,

Science of Total Environment, in press.

• Galli, A., Wiedmann, T., Ercin, A,E., Knoblauch, D., Ewing, B. and Giljum, S.

(2012b) Integrating ecological, carbon and water footprint into a “footprint family” of

indicators: Definition and role in tracking human pressure on the planet, Ecological

Indicators, 16: 100-112.

• Steen-Olsen, K., Weinzettel, J., Cranston G., Ercin, A.E and Hertwich E. (2012)

Carbon, land, and water footprint accounts for the European Union: Consumption,

276/ List of publications

production, and displacements through international trade, Environmental Science &

Technology, in press.

• Jefferies, D., Muñoz, I., Hodges, J., King, V.J., Aldaya, M., Ercin, A.E., Milà i Canals,

L. and Hoekstra, A.Y. (2012) Water Footprint and Life Cycle Assessment as

approaches to assess potential impacts of products on water consumption: Key learning

points from pilot studies on tea and margarine, Journal of Cleaner Production, 33:

155-166.

Books

• Ercin A.E (2006), Social Economic Assessment of South-eastern Anatolia Project,

METU Publications, Ankara, Turkey.

Research reports

• Ercin A.E, Aldaya M.M, Hoekstra A.Y (2009) A pilot in corporate water footprint

accounting and impact assessment, 'Value of Water Research Report Series No:39' of

UNESCO-IHE.

• Ercin, A.E., Aldaya, M.M. and Hoekstra, A.Y. (2011) The water footprint of soy milk

and soy burger and equivalent animal products, Value of Water Research Report Series

No.49, UNESCO-IHE.

• Ercin, A.E. and Hoekstra, A.Y. (2012a) Carbon and water Footprints: Similarities and

contrasts in concept, method and policy response, Side publication series:04, United

Nations World Water Assessment Programme, UNESCO, Paris, France.

• Ercin, A.E. and Hoekstra, A.Y. (2012b) Water footprint scenarios for 2050: A global

analysis and case study for Europe, Value of Water Research Report Series No. 59,


• Ercin, A.E., Mekonnen, M.M. and Hoekstra, A.Y. (2012a) The water footprint of

France, Value of Water Research Report Series No.56, UNESCO-IHE, the Netherlands

• Ercin, A.E., Mekonnen, M.M. and Hoekstra, A.Y. (2012b) The water footprint of

Switzerland, Value of Water Research Report Series No.57, UNESCO-IHE, the

Netherlands

277

About the author

Ertug Ercin was born in 23 June 1979 in Ankara, Turkey. He graduated from Ataturk

Anatolian High School in Ankara in 1997. During the last year of his high school

education, he was selected as a team member to represent Turkey at the International

Mathematical Olympiad. In national university entry exam, he was in top 100 among 1.5

million participants and enrolled to Middle East Technical University (METU), Ankara. He

obtained his BSc degree with distinction from METU in 2002. His specialization was

hydromechanics. After graduation, he started working as a design engineer. He was

responsible from design of water structures, like dams, water supply pipelines and

distribution systems. While working, he also started his MSc study in METU at the

Department of Hydromechanics. His area of focus in his MSc study was advanced

hydraulic system design and water distribution networks. He obtained his master degree,

with high distinction, in 2006. During his MSc study and afterwards, he worked for

international financial organizations and consulting companies as a water expert. He

specialized in water resources management, water supply and sanitation, waste

management, and sustainable development. In his past work, he participated in

internationally financed large-scale infrastructure and development projects as a water and

environmental expert. In 2008, he moved to the Netherlands and started his PhD at the

Water Engineering and Management Department, University of Twente. During his PhD,

he was responsible from execution of 9 different projects which were funded by EU, private

companies and NGOs. He was selected as a member of Young Scientific Committee of

World Water Week, Stockholm in 2012.

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